From 4ce9d8c84168c35535de0d2c109c8cf8ff4a2204 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Thu, 5 Dec 2024 07:54:52 -0600
Subject: [PATCH 001/345] feat: initial work to remove hardcoded filepaths

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/custom_types.py      |  11 +-
 main/como/rnaseq_preprocess.py | 678 +++++++++++++++++++--------------
 main/como/utils.py             |  57 +--
 3 files changed, 427 insertions(+), 319 deletions(-)

diff --git a/main/como/custom_types.py b/main/como/custom_types.py
index a4fcd19b..0f464203 100644
--- a/main/como/custom_types.py
+++ b/main/como/custom_types.py
@@ -1,4 +1,9 @@
+from __future__ import annotations
+
 from enum import Enum
+from pathlib import Path
+
+from fast_bioservices import Taxon
 
 
 class RNASeqPreparationMethod(Enum):
@@ -7,7 +12,7 @@ class RNASeqPreparationMethod(Enum):
     SCRNA = "scrna"
 
     @staticmethod
-    def from_string(value: str) -> "RNASeqPreparationMethod":
+    def from_string(value: str) -> RNASeqPreparationMethod:
         """Build a preparation method object from a string."""
         match_value = "".join(c for c in value if c.isascii()).lower()
 
@@ -21,3 +26,7 @@ def from_string(value: str) -> "RNASeqPreparationMethod":
             case _:
                 possible_values = [t.value for t in RNASeqPreparationMethod]
                 raise ValueError(f"Filtering technique must be one of {possible_values}; got: {value}")
+
+
+type_taxon = Taxon | int | str
+type_path = str | Path
diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index c522f0f1..ee02948c 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -4,27 +4,36 @@
 import asyncio
 import re
 from dataclasses import dataclass, field
+from io import StringIO
 from pathlib import Path
-from typing import Literal
+from typing import Literal, NamedTuple
 
+import aiofiles
 import numpy as np
 import pandas as pd
 import scanpy as sc
-from fast_bioservices import Input, Taxon
 from fast_bioservices.biothings.mygene import MyGene
 from loguru import logger
 
-from como import Config, stringlist_to_list
-from como.utils import convert_gene_data
+from como.custom_types import type_path, type_taxon
+from como.utils import _listify, convert_gene_data
 
+type_rna = Literal["total", "mrna"]
 
-@dataclass
-class _Arguments:
+
+class _Arguments(NamedTuple):
     context_names: list[str]
-    taxon_id: Taxon | int | str
-    mode: Literal["create", "provide"]
-    input_format: str
-    provided_matrix_fname: str = None
+    mode: list[Literal["create", "provide"]]
+    taxon_id: list[str]
+    input_como_dirpath: list[Path] | None
+    input_matrix_filepath: list[Path] | None
+    output_gene_info_filepath: Path | None
+    output_count_matrices_dir: list[Path] | None
+    output_trna_config_filepath: Path | None
+    output_mrna_config_filepath: Path | None
+    output_trna_count_matrix: list[Path] | None
+    output_mrna_count_matrix: list[Path] | None
+    cache: bool
 
 
 @dataclass
@@ -41,27 +50,28 @@ def num_genes(self):
         return len(self.count_matrix)
 
     @classmethod
-    def build_from_tab(cls, filepath: Path) -> _STARinformation:
+    async def build_from_tab(cls, filepath: Path) -> _STARinformation:
         if filepath.suffix != ".tab":
             raise ValueError(f"Building STAR information requires a '.tab' file; received: '{filepath}'")
-        with filepath.open("r") as i_stream:
-            num_unmapped = [int(i) for i in next(i_stream).rstrip("\n").split("\t")[1:]]
-            num_multimapping = [int(i) for i in next(i_stream).rstrip("\n").split("\t")[1:]]
-            num_no_feature = [int(i) for i in next(i_stream).rstrip("\n").split("\t")[1:]]
-            num_ambiguous = [int(i) for i in next(i_stream).rstrip("\n").split("\t")[1:]]
-
-        df = pd.read_csv(
-            filepath,
-            sep="\t",
-            skiprows=4,
-            names=[
-                "ensembl_gene_id",
-                "unstranded_rna_counts",
-                "first_read_transcription_strand",
-                "second_read_transcription_strand",
-            ],
-        )
-        # Remove NA values
+
+        async with aiofiles.open(filepath) as i_stream:
+            unmapped, multimapping, no_feature, ambiguous = await asyncio.gather(
+                *[i_stream.readline(), i_stream.readline(), i_stream.readline(), i_stream.readline()]
+            )
+            num_unmapped = [int(i) for i in unmapped.rstrip("\n").split("\t")[1:]]
+            num_multimapping = [int(i) for i in multimapping.rstrip("\n").split("\t")[1:]]
+            num_no_feature = [int(i) for i in no_feature.rstrip("\n").split("\t")[1:]]
+            num_ambiguous = [int(i) for i in ambiguous.rstrip("\n").split("\t")[1:]]
+            remainder = await i_stream.read()
+
+        string_io = StringIO(remainder)
+        df = pd.read_csv(string_io, sep="\t", header=None)
+        df.columns = [
+            "ensembl_gene_id",
+            "unstranded_rna_counts",
+            "first_read_transcription_strand",
+            "second_read_transcription_strand",
+        ]
         df = df[~df["ensembl_gene_id"].isna()]
         return _STARinformation(
             num_unmapped=num_unmapped,
@@ -77,7 +87,7 @@ def build_from_tab(cls, filepath: Path) -> _STARinformation:
 class _StudyMetrics:
     study_name: str
     count_files: list[Path]
-    strandedness_files: list[Path]
+    strand_files: list[Path]
     __sample_names: list[str] = field(default_factory=list)
     __num_samples: int = 0
 
@@ -93,10 +103,10 @@ def __post_init__(self):
         self.__num_samples = len(self.count_files)
         self.__sample_names = [f.stem for f in self.count_files]
 
-        if len(self.count_files) != len(self.strandedness_files):
+        if len(self.count_files) != len(self.strand_files):
             raise ValueError(
                 f"Unequal number of count files and strand files for study '{self.study_name}'. "
-                f"Found {len(self.count_files)} count files and {len(self.strandedness_files)} strand files."
+                f"Found {len(self.count_files)} count files and {len(self.strand_files)} strand files."
             )
 
         if self.num_samples != len(self.count_files):
@@ -105,44 +115,36 @@ def __post_init__(self):
                 f"Found {self.num_samples} samples and {len(self.count_files)} count files."
             )
 
-        if self.num_samples != len(self.strandedness_files):
+        if self.num_samples != len(self.strand_files):
             raise ValueError(
                 f"Unequal number of samples and strand files for study '{self.study_name}'. "
-                f"Found {self.num_samples} samples and {len(self.strandedness_files)} strand files."
+                f"Found {self.num_samples} samples and {len(self.strand_files)} strand files."
             )
 
         if self.__num_samples == 1:
             raise ValueError(f"Only one sample exists for study {self.study_name}. Provide at least two samples")
 
         self.count_files.sort()
-        self.strandedness_files.sort()
+        self.strand_files.sort()
         self.__sample_names.sort()
 
 
-def _context_from_filepath(file: Path) -> str:
-    return file.stem.split("_S")[0]
-
-
 def _sample_name_from_filepath(file: Path) -> str:
     return re.search(r".+_S\d+R\d+", file.stem).group()
 
 
 def _organize_gene_counts_files(data_dir: Path) -> list[_StudyMetrics]:
-    root_gene_count_dir = Path(data_dir, "geneCounts")
-    root_strandedness_dir = Path(data_dir, "strandedness")
+    gene_count_dir = Path(data_dir, "geneCounts").resolve()
+    strand_dir = Path(data_dir, "strandedness").resolve()
 
-    gene_counts_directories: list[Path] = sorted(
-        [p for p in Path(root_gene_count_dir).glob("*") if not p.name.startswith(".")]
-    )
-    strandedness_directories: list[Path] = sorted(
-        [p for p in Path(root_strandedness_dir).glob("*") if not p.name.startswith(".")]
-    )
+    gene_counts_directories: list[Path] = sorted([p for p in gene_count_dir.glob("*") if not p.name.startswith(".")])
+    strandedness_directories: list[Path] = sorted([p for p in strand_dir.glob("*") if not p.name.startswith(".")])
 
     if len(gene_counts_directories) != len(strandedness_directories):
         raise ValueError(
             f"Unequal number of gene count directories and strandedness directories. "
             f"Found {len(gene_counts_directories)} gene count directories and {len(strandedness_directories)} strandedness directories."  # noqa: E501
-            f"\nGene count directory: {root_gene_count_dir}\nStrandedness directory: {root_strandedness_dir}"
+            f"\nGene count directory: {gene_count_dir}\nStrandedness directory: {strand_dir}"
         )
 
     # For each study, collect gene count files, fragment files, insert size files, layouts, and strandedness information
@@ -157,16 +159,16 @@ def _organize_gene_counts_files(data_dir: Path) -> list[_StudyMetrics]:
             _StudyMetrics(
                 study_name=gene_dir.stem,
                 count_files=list(gene_dir.glob("*.tab")),
-                strandedness_files=list(strand_dir.glob("*.txt")),
+                strand_files=list(strand_dir.glob("*.txt")),
             )
         )
     return study_metrics
 
 
-def _process_first_multirun_sample(strand_file: Path, all_counts_files: list[Path]):
+async def _process_first_multirun_sample(strand_file: Path, all_counts_files: list[Path]):
     sample_count = pd.DataFrame()
     for file in all_counts_files:
-        star_information = _STARinformation.build_from_tab(file)
+        star_information = await _STARinformation.build_from_tab(file)
         strand_information = strand_file.read_text().rstrip("\n").lower()
 
         if strand_information not in ("none", "first_read_transcription_strand", "second_read_transcription_strand"):
@@ -196,8 +198,8 @@ def _process_first_multirun_sample(strand_file: Path, all_counts_files: list[Pat
     return count_sums
 
 
-def _process_standard_replicate(counts_file: Path, strand_file: Path, sample_name: str):
-    star_information = _STARinformation.build_from_tab(counts_file)
+async def _process_standard_replicate(counts_file: Path, strand_file: Path, sample_name: str):
+    star_information = await _STARinformation.build_from_tab(counts_file)
     strand_information = strand_file.read_text().rstrip("\n").lower()
 
     if strand_information not in ("none", "first_read_transcription_strand", "second_read_transcription_strand"):
@@ -214,7 +216,7 @@ def _process_standard_replicate(counts_file: Path, strand_file: Path, sample_nam
     return sample_count
 
 
-def _prepare_sample_counts(
+async def _prepare_sample_counts(
     sample_name: str,
     counts_file: Path,
     strand_file: Path,
@@ -222,27 +224,27 @@ def _prepare_sample_counts(
 ) -> pd.DataFrame | Literal["SKIP"]:
     # Test if the counts_file is the first run in a multi-run smaple
     if re.search(r"R\d+r1", counts_file.as_posix()):
-        return _process_first_multirun_sample(strand_file=strand_file, all_counts_files=all_counts_files)
+        return await _process_first_multirun_sample(strand_file=strand_file, all_counts_files=all_counts_files)
     elif re.search(r"R\d+r\d+", counts_file.as_posix()):
         return "SKIP"
     else:
-        return _process_standard_replicate(counts_file, strand_file, sample_name)
+        return await _process_standard_replicate(counts_file, strand_file, sample_name)
 
 
 async def _create_sample_counts_matrix(metrics: _StudyMetrics) -> pd.DataFrame:
     adjusted_index = 0
-    counts: pd.DataFrame | Literal["SKIP"] = _prepare_sample_counts(
+    counts: pd.DataFrame | Literal["SKIP"] = await _prepare_sample_counts(
         sample_name=metrics.sample_names[0],
         counts_file=metrics.count_files[0],
-        strand_file=metrics.strandedness_files[0],
+        strand_file=metrics.strand_files[0],
         all_counts_files=metrics.count_files,
     )
 
     for i in range(1, metrics.num_samples):
-        new_counts = _prepare_sample_counts(
+        new_counts = await _prepare_sample_counts(
             sample_name=metrics.sample_names[i],
             counts_file=metrics.count_files[i],
-            strand_file=metrics.strandedness_files[i],
+            strand_file=metrics.strand_files[i],
             all_counts_files=metrics.count_files,
         )
         if isinstance(new_counts, str) and new_counts == "SKIP":
@@ -260,35 +262,38 @@ async def _create_sample_counts_matrix(metrics: _StudyMetrics) -> pd.DataFrame:
     return counts
 
 
-async def _create_counts_matrix(context_name: str, config: Config):
-    """Create a counts matrix by reading gene counts table(s)."""
-    data_dir = config.data_dir / "COMO_input" / context_name
-    matrix_output_dir = config.data_dir / "data_matrices" / context_name
-
-    study_metrics = _organize_gene_counts_files(data_dir=data_dir)
-    final_matrix: pd.DataFrame = pd.DataFrame()
+async def _write_counts_matrix(
+    *,
+    config_df: pd.DataFrame,
+    como_context_dir: Path,
+    output_counts_matrix_filepath: Path,
+    rna_type: type_rna,
+) -> pd.DataFrame:
+    """Create a counts matrix file by reading gene counts table(s)."""
+    study_metrics = _organize_gene_counts_files(data_dir=como_context_dir)
+    counts: list[pd.DataFrame] = await asyncio.gather(
+        *[_create_sample_counts_matrix(metric) for metric in study_metrics]
+    )
+    final_matrix = pd.DataFrame()
+    for count in counts:
+        final_matrix = count if final_matrix.empty else pd.merge(final_matrix, count, on="ensembl_gene_id", how="outer")
 
-    for metric in study_metrics:
-        counts: pd.DataFrame = await _create_sample_counts_matrix(metric)
-        final_matrix = (
-            counts if final_matrix.empty else pd.merge(final_matrix, counts, on="ensembl_gene_id", how="outer")
-        )
+    rna_specific_sample_names = config_df.loc[config_df["library_prep"] == rna_type, "sample_name"].tolist()
+    final_matrix = final_matrix[["ensembl_gene_id", *rna_specific_sample_names]]
 
-    output_filename = matrix_output_dir / f"gene_counts_matrix_full_{data_dir.stem}.csv"
-    output_filename.parent.mkdir(parents=True, exist_ok=True)
-    final_matrix.to_csv(output_filename, index=False)
-    logger.success(f"Wrote gene count matrix for '{data_dir.stem}' at '{output_filename}'")
+    output_counts_matrix_filepath.parent.mkdir(parents=True, exist_ok=True)
+    final_matrix.to_csv(output_counts_matrix_filepath, index=False)
+    logger.success(f"Wrote gene count matrix at '{output_counts_matrix_filepath}'")
+    return final_matrix
 
 
-async def _create_config_df(context_name: str) -> pd.DataFrame:  # noqa: C901
+async def _create_config_df(context_name: str, /, como_input_dir: Path) -> pd.DataFrame:  # noqa: C901
     """Create configuration sheet.
 
     The configuration file created is based on the gene counts matrix.
      If using zFPKM normalization technique, mean fragment lengths will be fetched
     """
-    config = Config()
-    gene_counts_files = list(Path(config.data_dir, "COMO_input", context_name, "geneCounts").rglob("*.tab"))
-
+    gene_counts_files = list(Path(como_input_dir, context_name, "geneCounts").rglob("*.tab"))
     sample_names: list[str] = []
     fragment_lengths: list[int | float] = []
     layouts: list[str] = []
@@ -329,19 +334,12 @@ async def _create_config_df(context_name: str) -> pd.DataFrame:  # noqa: C901
                     r_label = re.findall(r"r\d{1,3}", r.as_posix())[0]
                     R_label = re.findall(r"R\d{1,3}", r.as_posix())[0]  # noqa: N806
                     frag_filename = "".join([context_name, "_", study_number, R_label, r_label, "_fragment_size.txt"])
-                    frag_files.append(
-                        config.data_dir / "COMO_input" / context_name / "fragmentSizes" / study_number / frag_filename
-                    )
-
-        context_path = config.data_dir / "COMO_input" / context_name
+                    frag_files.append(como_input_dir / context_name / "fragmentSizes" / study_number / frag_filename)
 
+        context_path = como_input_dir / context_name
         layout_files: list[Path] = list((context_path / "layouts").rglob(f"{context_name}_{label}_layout.txt"))
-        strand_files: list[Path] = list(
-            (context_path / "strandedness").rglob(f"{context_name}_{label}_strandedness.txt")
-        )
-        frag_files: list[Path] = list(
-            (context_path / "fragmentSizes").rglob(f"{context_name}_{label}_fragment_size.txt")
-        )
+        strand_files: list[Path] = list((context_path / "strandedness").rglob(f"{context_name}_{label}_strandedness.txt"))  # fmt: skip  # noqa: E501
+        frag_files: list[Path] = list((context_path / "fragmentSizes").rglob(f"{context_name}_{label}_fragment_size.txt"))  # fmt: skip  # noqa: E501
         prep_files: list[Path] = list((context_path / "prepMethods").rglob(f"{context_name}_{label}_prep_method.txt"))
 
         layout = "UNKNOWN"
@@ -351,7 +349,7 @@ async def _create_config_df(context_name: str) -> pd.DataFrame:  # noqa: C901
                 f"this should be defined by user if using zFPKM or rnaseq_gen.py will not run"
             )
         elif len(layout_files) == 1:
-            with layout_files[0].open("w") as file:
+            with layout_files[0].open("r") as file:
                 layout = file.read().strip()
         elif len(layout_files) > 1:
             raise ValueError(
@@ -367,7 +365,7 @@ async def _create_config_df(context_name: str) -> pd.DataFrame:  # noqa: C901
                 f"infer the strandedness when writing the counts matrix"
             )
         elif len(strand_files) == 1:
-            with strand_files[0].open("w") as file:
+            with strand_files[0].open("r") as file:
                 strand = file.read().strip()
         elif len(strand_files) > 1:
             raise ValueError(
@@ -379,7 +377,7 @@ async def _create_config_df(context_name: str) -> pd.DataFrame:  # noqa: C901
         if len(prep_files) == 0:
             logger.warning(f"No prep file found for {label}, assuming 'total' as in Total RNA library preparation")
         elif len(prep_files) == 1:
-            with prep_files[0].open("w") as file:
+            with prep_files[0].open("r") as file:
                 prep = file.read().strip().lower()
                 if prep not in ["total", "mrna"]:
                     raise ValueError(f"Prep method must be either 'total' or 'mrna' for {label}")
@@ -441,55 +439,29 @@ async def _create_config_df(context_name: str) -> pd.DataFrame:  # noqa: C901
     return out_df
 
 
-def _split_config_df(df):
-    """Split a config dataframe to two.
-
-    One for Total RNA library prep, one for mRNA
-    """
-    df_t = df[df["library_prep"] == "total"]
-    df_m = df[df["library_prep"] == "mrna"]
-
-    return df_t, df_m
-
-
-def _split_counts_matrices(
-    count_matrix_all: Path, df_total: pd.DataFrame, df_mrna: pd.DataFrame
-) -> tuple[pd.DataFrame, pd.DataFrame]:
-    """Split a counts-matrix dataframe to two.
-
-    One for Total RNA library prep, one for mRNA
-    """
-    logger.info(f"Reading gene count matrix file at '{count_matrix_all}'")
-    matrix_all = pd.read_csv(count_matrix_all)
-    matrix_total = matrix_all[
-        ["ensembl_gene_id"] + [n for n in matrix_all.columns if n in df_total["sample_name"].tolist()]
-    ]
-    matrix_mrna = matrix_all[
-        ["ensembl_gene_id"] + [n for n in matrix_all.columns if n in df_mrna["sample_name"].tolist()]
-    ]
-
-    return matrix_total, matrix_mrna
-
-
 async def _create_gene_info_file(
     *,
-    matrix_files: list[Path],
-    taxon_id,
-    config: Config,
+    counts_matrix: pd.DataFrame | Path,
+    output_filepath: Path,
+    taxon_id: type_taxon,
     cache: bool,
 ):
     """Create gene info file for specified context by reading first column in its count matrix file."""
     logger.info("Fetching gene info")
-    genes = set()
-    for file in matrix_files:
-        data: pd.DataFrame | sc.AnnData = pd.read_csv(file) if file.suffix == ".csv" else sc.read_h5ad(file)
-        input_values = data.iloc[:, 0].tolist() if isinstance(data, pd.DataFrame) else data.var_names.tolist()
-        conversion = await convert_gene_data(input_values, taxon_id)
-        genes.update(conversion["entrez_gene_id"].astype(str).tolist())
+
+    data: pd.DataFrame | sc.AnnData = (
+        (pd.read_csv(counts_matrix) if counts_matrix.suffix == ".csv" else sc.read_h5ad(counts_matrix))
+        if isinstance(counts_matrix, Path)
+        else counts_matrix
+    )
+
+    input_values = data.iloc[:, 0].tolist() if isinstance(data, pd.DataFrame) else data.var_names.tolist()
+    conversion = await convert_gene_data(input_values, taxon_id)
+    genes = conversion["entrez_gene_id"].astype(str).tolist()
 
     mygene = MyGene(cache=cache)
     gene_data = await mygene.query(
-        items=list(genes),
+        items=genes,
         taxon=taxon_id,
         scopes="entrezgene",
     )
@@ -521,123 +493,222 @@ async def _create_gene_info_file(
     ]
     gene_info["size"] = gene_info["end_position"].astype(int) - gene_info["start_position"].astype(int)
     gene_info.drop(columns=["start_position", "end_position"], inplace=True)
-    output_filepath = config.data_dir / "gene_info.csv"
     gene_info.to_csv(output_filepath, index=False)
     logger.success(f"Gene Info file written at '{output_filepath}'")
 
 
-async def _handle_context_batch(  # noqa: C901
+async def _create_matrix_file(
+    context_name: str,
+    taxon_id: type_taxon,
+    output_gene_info_filepath: Path,
+    output_config_filepath: Path,
+    output_gene_matrix_filepath: Path,
+    como_dirpath: type_path,
+    rna_type: type_rna,
+    cache: bool,
+) -> None:
+    como_context_dir = como_dirpath / context_name
+    config_df = await _create_config_df(context_name, como_input_dir=como_dirpath)
+    counts_matrix = await _write_counts_matrix(
+        config_df=config_df,
+        como_context_dir=como_context_dir,
+        output_counts_matrix_filepath=output_gene_matrix_filepath,
+        rna_type=rna_type,
+    )
+    with pd.ExcelWriter(output_config_filepath) as writer:
+        subset_config = config_df[config_df["library_prep"] == rna_type]
+        subset_config.to_excel(writer, sheet_name=context_name, header=True, index=False)
+
+    await _create_gene_info_file(
+        counts_matrix=counts_matrix,
+        output_filepath=output_gene_info_filepath,
+        taxon_id=taxon_id,
+        cache=cache,
+    )
+
+
+async def _process_items(
     context_names: list[str],
-    mode: Literal["create", "provide"],
-    taxon_id,
-    provided_matrix_file,
-    config: Config,
+    mode: list[Literal["create", "provide"]],
+    taxon_id: list[str],
+    input_como_dirpath: list[Path] | None,
+    input_matrix_filepath: list[Path] | None,
+    output_gene_info_filepath: Path,
+    output_trna_config_filepath: Path | None,
+    output_mrna_config_filepath: Path | None,
+    output_trna_count_matrix: list[Path] | None,
+    output_mrna_count_matrix: list[Path] | None,
     cache: bool,
 ):
-    """Handle iteration through each context type and create appropriate files."""
-    trnaseq_config_filename = config.config_dir / "trnaseq_data_inputs_auto.xlsx"
-    mrnaseq_config_filename = config.config_dir / "mrnaseq_data_inputs_auto.xlsx"
-
-    using_trna = False  # turn on when any total set is found to prevent writer from being init multiple times or empty
-    using_mrna = False  # turn on when any mrna set is found to prevent writer from being init multiple times or empty
-
-    logger.success(f"Found {len(context_names)} contexts to process: {', '.join(context_names)}")
-
-    tmatrix_files: list[Path] = []
-    mmatrix_files: list[Path] = []
-    match mode:
-        case "create":
-            for context_name in context_names:
-                context_name = context_name.strip(" ")
-                logger.info(f"Processing {context_name}")
-                gene_output_dir = config.result_dir / context_name
-                matrix_output_dir = config.data_dir / "data_matrices" / context_name
-
-                gene_output_dir.parent.mkdir(parents=True, exist_ok=True)
-                matrix_output_dir.parent.mkdir(parents=True, exist_ok=True)
-
-                logger.info(f"Gene info output directory is '{gene_output_dir}'")
-
-                await _create_counts_matrix(context_name, config=config)
-                # TODO: warn user or remove samples that are all 0 to prevent density plot error in zFPKM
-                config_df = await _create_config_df(context_name)
-                trna_df, mrna_df = _split_config_df(config_df)
-
-                matrix_path_total = matrix_output_dir / f"gene_counts_matrix_total_{context_name}.csv"
-                if not trna_df.empty:
-                    if not using_trna:
-                        using_trna = True
-                        twriter = pd.ExcelWriter(trnaseq_config_filename)
-
-                    tmatrix_files.append(matrix_path_total)
-                    trna_df.to_excel(twriter, sheet_name=context_name, header=True, index=False)
-
-                matrix_path_mrna = matrix_output_dir / f"gene_counts_matrix_mrna_{context_name}.csv"
-                if not mrna_df.empty:
-                    if not using_mrna:
-                        using_mrna = True
-                        mwriter = pd.ExcelWriter(mrnaseq_config_filename)
-
-                    mmatrix_files.append(matrix_path_mrna)
-                    mrna_df.to_excel(mwriter, sheet_name=context_name, header=True, index=False)
-
-                matrix_path_all = matrix_output_dir / f"gene_counts_matrix_full_{context_name}.csv"
-                trna_matrix, mrna_matrix = _split_counts_matrices(matrix_path_all, trna_df, mrna_df)
-                if len(trna_matrix.columns) >= 1:
-                    trna_matrix.to_csv(matrix_path_total, header=True, index=False)
-                if len(mrna_matrix.columns) >= 1:
-                    mrna_matrix.to_csv(matrix_path_mrna, header=True, index=False)
-
-            if using_trna:
-                twriter.close()
-            if using_mrna:
-                mwriter.close()
-
-            await _create_gene_info_file(
-                matrix_files=tmatrix_files + mmatrix_files,
-                taxon_id=taxon_id,
-                config=config,
-                cache=cache,
+    tasks = []
+    for i, m in enumerate(mode):
+        if m == "create" and output_trna_config_filepath:
+            tasks.append(
+                asyncio.create_task(
+                    _create_matrix_file(
+                        context_name=context_names[i],
+                        taxon_id=taxon_id[i],
+                        output_gene_matrix_filepath=output_trna_count_matrix[i],
+                        como_dirpath=input_como_dirpath[i],
+                        output_gene_info_filepath=output_gene_info_filepath,
+                        output_config_filepath=output_trna_config_filepath,
+                        rna_type="total",
+                        cache=cache,
+                    )
+                )
             )
-        case "provide":
-            matrix_files: list[Path] = [Path(p) for p in stringlist_to_list(provided_matrix_file)]
-            await _create_gene_info_file(
-                matrix_files=matrix_files,
-                taxon_id=taxon_id,
-                config=config,
-                cache=cache,
+
+        if m == "create" and output_mrna_config_filepath:
+            tasks.append(
+                asyncio.create_task(
+                    _create_matrix_file(
+                        context_name=context_names[i],
+                        taxon_id=taxon_id[i],
+                        output_gene_matrix_filepath=output_mrna_count_matrix[i],
+                        como_dirpath=input_como_dirpath[i],
+                        output_gene_info_filepath=output_gene_info_filepath,
+                        output_config_filepath=output_mrna_config_filepath,
+                        rna_type="mrna",
+                        cache=cache,
+                    )
+                )
             )
-        case _:
-            raise ValueError("'--mode' must be either 'create' or 'provide'")
 
+        if m == "provide":
+            tasks.append(
+                asyncio.create_task(
+                    _create_gene_info_file(
+                        counts_matrix=input_matrix_filepath[i],
+                        output_filepath=output_gene_info_filepath,
+                        taxon_id=taxon_id[i],
+                        cache=cache,
+                    )
+                )
+            )
+    await asyncio.gather(*tasks)
 
-async def rnaseq_preprocess(
-    context_names: list[str],
-    mode: Literal["create", "provide"],
-    taxon_id: int | str,
-    input_format: Input | str,
-    matrix_file: str | Path | None = None,
-    config: Config = None,
+
+def _validate_matrix_output_args(
+    output_count_matrices_dirpath: list,
+    output_trna_count_matrix_filepath: list,
+    output_mrna_count_matrix_filepath: list,
+):
+    def _raise():
+        raise ValueError(
+            "output_count_matrices_dirpath OR "
+            "(output_trna_count_matrix_filepath AND output_mrna_count_matrix_filepath) can be provided"
+        )
+
+    # output_count_matrices_dir OR (output_trna_count_matrix AND output_mrna_count_matrix) can be provided
+    # Check this condition is satisfied
+    if output_count_matrices_dirpath and (output_trna_count_matrix_filepath or output_mrna_count_matrix_filepath):
+        _raise()
+    if output_trna_count_matrix_filepath and not output_mrna_count_matrix_filepath:
+        _raise()
+    if not output_trna_count_matrix_filepath and output_mrna_count_matrix_filepath:
+        _raise()
+
+
+async def rnaseq_preprocess(  # noqa: C901
+    context_names: str | list[str],
+    mode: Literal["create", "provide"] | list[Literal["create", "provide"]],
+    taxon_id: type_taxon | list[type_taxon],
+    input_como_dirpath: type_path | list[type_path] | None = None,
+    input_matrix_filepath: type_path | list[type_path] | None = None,
+    output_gene_info_filepath: Path | None = None,
+    output_trna_config_filepath: Path | None = None,
+    output_mrna_config_filepath: Path | None = None,
+    output_count_matrices_dirpath: list[Path] | None = None,
+    output_trna_count_matrix: list[Path] | None = None,
+    output_mrna_count_matrix: list[Path] | None = None,
     cache: bool = True,
 ) -> None:
     """Preprocesses RNA-seq data for downstream analysis.
 
     Fetches additional gene information from a provided matrix or gene counts,
         or optionally creates this matrix using gene count files obtained using STAR aligner
+
+    :param context_names: The context/cell type being processed
+    :param mode: The mode of operation
+    :param taxon_id: The NCBI taxonomy ID
+    :param output_gene_info_filepath: Path to the output gene information CSV file
+    :param output_trna_config_filepath: Path to the output tRNA config file (if in "create" mode)
+    :param output_mrna_config_filepath: Path to the output mRNA config file (if in "create" mode)
+    :param output_count_matrices_dirpath: The path to write all created count matrices
+    :param output_trna_count_matrix: The path to write total RNA count matrices
+    :param output_mrna_count_matrix: The path to write messenger RNA count matrices
+    :param input_como_dirpath: If in "create" mode, the input path(s) to the COMO_input directory of the current context
+        i.e., the directory containing "fragmentSizes", "geneCounts", "insertSizeMetrics", etc. directories
+    :param input_matrix_filepath: If in "provide" mode, the path(s) to the count matrices to be processed
+    :param cache: Should HTTP requests be cached
     """
-    config = Config() if config is None else config
-    if mode not in {"create", "provide"}:
-        raise ValueError("mode must be either 'create' or 'provide'")
+    context_names = _listify(context_names)
+    mode = _listify(mode)
+    taxon_id = _listify(taxon_id)
+    output_count_matrices_dirpath: list[Path] = [Path(i) for i in _listify(output_count_matrices_dirpath)] if output_count_matrices_dirpath else []  # fmt: skip  # noqa: E501
+    output_trna_count_matrix: list[Path] = [Path(i) for i in _listify(output_trna_count_matrix)] if output_trna_count_matrix else []  # fmt: skip  # noqa: E501
+    output_mrna_count_matrix: list[Path] = [Path(i) for i in _listify(output_mrna_count_matrix)] if output_mrna_count_matrix else []  # fmt: skip  # noqa: E501
+    input_como_dirpath: list[Path] = [Path(i) for i in _listify(input_como_dirpath)] if input_como_dirpath else []
+    input_matrix_filepath: list[Path] = (
+        [Path(i) for i in _listify(input_matrix_filepath)] if input_matrix_filepath else []
+    )
 
-    if not isinstance(taxon_id, int) and taxon_id not in ["human", "mouse"]:
-        raise ValueError("taxon_id must be either an integer, or accepted string ('mouse', 'human')")
+    _validate_matrix_output_args(
+        output_count_matrices_dirpath=output_count_matrices_dirpath,
+        output_trna_count_matrix_filepath=output_trna_count_matrix,
+        output_mrna_count_matrix_filepath=output_mrna_count_matrix,
+    )
+
+    if len(input_como_dirpath) == 0 and len(input_matrix_filepath) == 0:
+        raise ValueError("Either 'como_input_dirpath' or 'input_matrix_filepath' must be provided.")
+
+    if not any({output_trna_config_filepath, output_mrna_config_filepath}):
+        raise ValueError("Either 'output_trna_config_filepath' or 'output_mrna_config_filepath' must be provided.")
+    if output_trna_config_filepath and output_trna_config_filepath.suffix not in {".xlsx", ".xls"}:
+        raise ValueError("output_trna_config_filepath must be an Excel file.")
+    if output_mrna_config_filepath and output_mrna_config_filepath.suffix not in {".xlsx", ".xls"}:
+        raise ValueError("output_mrna_config_filepath must be an Excel file.")
+
+    if not all(m in {"create", "provide"} for m in mode):
+        raise ValueError(f"Invalid mode(s): {', '.join(m for m in mode if m not in {'create', 'provide'})}")
+
+    if not all(t.isdigit() or isinstance(t, int) or t in {"human", "mouse"} for t in taxon_id):
+        raise ValueError("Invalid taxon_id(s). Must be integer, 'human', or 'mouse'.")
+
+    if not (len(context_names) == len(mode) == len(taxon_id) == len(input_como_dirpath or input_matrix_filepath)):
+        raise ValueError(
+            "context_names, mode, taxon_id, and (como or matrix) input must be the same length.\n"
+            f"context_names: {len(context_names)}\n"
+            f"mode: {len(mode)}\n"
+            f"taxon_id: {len(taxon_id)}\n"
+            f"como_input_dirpath or matrix_filepath: {len(input_como_dirpath or input_matrix_filepath)}"
+        )
 
-    await _handle_context_batch(
+    for path in input_como_dirpath:
+        if not path.exists():
+            raise ValueError(f"COMO input directory does not exist: {path}")
+        if not path.is_dir():
+            raise ValueError(f"COMO input directory must be a directory: {path}")
+
+    for path in input_matrix_filepath:
+        if not path.exists():
+            raise ValueError(f"Input matrix file does not exist: {path}")
+        if path.suffix not in {".csv", ".h5ad"}:
+            raise ValueError(f"Input matrix file must be a .csv or .h5ad file: {path}")
+        if not path.is_file():
+            raise ValueError(f"Input matrix file must be a file: {path}")
+
+    await _process_items(
         context_names=context_names,
         mode=mode,
         taxon_id=taxon_id,
-        provided_matrix_file=Path(matrix_file if matrix_file is not None else "").as_posix(),
-        config=config,
+        output_gene_info_filepath=output_gene_info_filepath,
+        output_trna_config_filepath=output_trna_config_filepath,
+        output_mrna_config_filepath=output_mrna_config_filepath,
+        output_trna_count_matrix=output_trna_count_matrix,
+        output_mrna_count_matrix=output_mrna_count_matrix,
+        input_como_dirpath=input_como_dirpath,
+        input_matrix_filepath=input_matrix_filepath,
         cache=cache,
     )
 
@@ -645,97 +716,120 @@ async def rnaseq_preprocess(
 def _parse_args():
     parser = argparse.ArgumentParser(
         prog="rnaseq_preprocess.py",
-        description="""
-            Fetches additional gene information from a provided matrix or gene counts, or optionally creates this
-            matrix using gene count files obtained using STAR aligner. Creation of counts matrix from STAR aligner
-            output requires that the 'COMO_input' folder exists and is correctly structured according to the
-            normalization technique being used. A correctly structured folder can be made using our Snakemake-based
-            alignment pipeline at:
-            https://github.com/HelikarLab/FastqToGeneCounts""",
-        epilog="""
-            For additional help, please post questions/issues in the MADRID GitHub repo at
-            https://github.com/HelikarLab/MADRID or email babessell@gmail.com""",
+        description="Fetches additional gene information from a provided matrix or gene counts, "
+        "or optionally creates this matrix using gene count files obtained using STAR aligner. "
+        "Creation of counts matrix from STAR aligner output requires that the 'COMO_input' "
+        "folder exists and is correctly structured according to the normalization technique being used. "
+        "A correctly structured folder can be made using our Snakemake-based alignment pipeline at:"
+        "https://github.com/HelikarLab/FastqToGeneCounts",
+        epilog="For additional help, please post questions/issues in the MADRID GitHub repo at"
+        "https://github.com/HelikarLab/COMO",
     )
     parser.add_argument(
-        "-n",
         "--context-names",
+        required=True,
         type=str,
-        nargs="+",
+        nargs="*",
+        help="Tissue/cell name of models to generate. These names should correspond to the folders"
+        "in 'COMO_input/' if creating count matrix files, or to"
+        "'work/data/data_matrices/<context name>/gene_counts_matrix_<context name>.csv' if supplying"
+        "the count matrix as an imported .csv file. If making multiple models in a batch, then"
+        "use the format: 'context1 context2 context3'",
+    )
+    parser.add_argument(
+        "--mode",
+        type=str,
+        nargs="*",
         required=True,
-        dest="context_names",
-        help="""Tissue/cell name of models to generate. These names should correspond to the folders
-                             in 'COMO_input/' if creating count matrix files, or to
-                             'work/data/data_matrices/<context name>/gene_counts_matrix_<context name>.csv' if supplying
-                             the count matrix as an imported .csv file. If making multiple models in a batch, then
-                             use the format: "context1 context2 context3". """,
+        help="Mode of rnaseq_preprocess.py, either 'create' or 'provide'",
     )
     parser.add_argument(
-        "-i",
         "--taxon-id",
         required=False,
-        default=9606,
-        dest="taxon_id",
+        nargs="*",
+        type=str,
+        default="9606",
         help="BioDbNet taxon ID number, also accepts 'human', or 'mouse'",
     )
     parser.add_argument(
-        "--input-format",
-        required=True,
-        dest="input_format",
-        help="The data input format, such as Ensembl, Entrez, etc.",
+        "--output-gene-info-filepath",
+        required=False,
+        type=Path,
+        help="The location to write gene information",
     )
     parser.add_argument(
-        "--mode",
+        "--output-count-matrices-dir",
+        required=False,
         type=str,
-        required=True,
-        dest="mode",
-        choices={"create", "provide"},
-        help="Mode of rnaseq_preprocess.py, either 'make' or 'provide'",
+        help="All count matrix files can be placed in a single directory "
+        "if they should not be saved to specific locations",
+    )
+    parser.add_argument(
+        "--output-trna-count-matrix",
+        required=False,
+        type=str,
+        help="The location to save total RNA count matrices",
+    )
+    parser.add_argument(
+        "--output-mrna-count-matrix",
+        required=False,
+        type=str,
+        help="The location to save messenger RNA count matrices",
     )
     parser.add_argument(
-        "--matrix",
+        "--output-trna-config-filepath",
         required=False,
-        dest="provided_matrix_fname",
         default=None,
-        help="Name of provided counts matrix in /work/data/data_matrices/<context name>/<NAME OF FILE>.csv",
+        type=Path,
+        help="The location to save TRNA config file",
     )
-
-    parsed = parser.parse_args()
-    parsed.context_names = stringlist_to_list(parsed.context_names)
-
-    if parsed.mode == "provide" and parsed.provided_matrix_fname is None:
-        raise ValueError("If provide_matrix is True, then provided_matrix_fname must be provided")
-
-    # handle species alternative ids
-    taxon_id = str(parsed.taxon_id)
-    if taxon_id.isdigit():
-        try:
-            parsed.taxon_id = Taxon.from_int(int(taxon_id))
-        except ValueError:
-            parsed.taxon_id = int(taxon_id)
-    else:
-        if taxon_id.upper() in {"HUMAN", "HOMO SAPIENS"}:
-            parsed.taxon_id = Taxon.HOMO_SAPIENS
-        elif taxon_id.upper() in {"MOUSE", "MUS MUSCULUS"}:
-            parsed.taxon_id = Taxon.MUS_MUSCULUS
-        else:
-            raise ValueError(
-                f"Taxon id (--taxon-id) is invalid; accepts 'human', 'mouse', or an integer value; provided: {taxon_id}"
-            )
-
-    args = _Arguments(**vars(parsed))
-    return args
+    parser.add_argument(
+        "--output-mrna-config-filepath",
+        required=False,
+        default=None,
+        type=Path,
+        help="The location to save MRNA config file",
+    )
+    parser.add_argument(
+        "--input-como-dirpath",
+        nargs="*",
+        required=False,
+        default=None,
+        type=str,
+        help="Path to COMO input directory",
+    )
+    parser.add_argument(
+        "--input-matrix-filepath",
+        required=False,
+        nargs="*",
+        default=None,
+        type=str,
+        help="Path to input matrix file",
+    )
+    parser.add_argument(
+        "--cache",
+        required=False,
+        type=bool,
+        default=True,
+        help="Cache files for faster processing",
+    )
+    return _Arguments(**vars(parser.parse_args()))
 
 
 if __name__ == "__main__":
     args: _Arguments = _parse_args()
-    taxon_id_value = args.taxon_id.value if isinstance(args.taxon_id, Taxon) else args.taxon_id
-
     asyncio.run(
         rnaseq_preprocess(
             context_names=args.context_names,
             mode=args.mode,
-            taxon_id=taxon_id_value,
-            matrix_file=args.provided_matrix_fname,
-            input_format=args.input_format,
+            taxon_id=args.taxon_id,
+            output_gene_info_filepath=args.output_gene_info_filepath,
+            output_trna_count_matrix=args.output_trna_count_matrix,
+            output_mrna_count_matrix=args.output_mrna_count_matrix,
+            output_trna_config_filepath=args.output_trna_config_filepath,
+            output_mrna_config_filepath=args.output_mrna_config_filepath,
+            input_como_dirpath=args.input_como_dirpath,
+            input_matrix_filepath=args.input_matrix_filepath,
+            cache=args.cache,
         )
     )
diff --git a/main/como/utils.py b/main/como/utils.py
index 9668a34d..a359de0f 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -115,32 +115,32 @@ def stringlist_to_list(stringlist: str | list[str]) -> list[str]:
 
     :param stringlist: The "string list" gathered from the command line. Example input: "['mat', 'xml', 'json']"
     """
-    if isinstance(stringlist, str):
-        if stringlist.startswith("[") and stringlist.endswith("]"):
-            # Remove any brackets from the first and last items; replace quotation marks and commas with nothing
-            new_list: list[str] = stringlist.strip("[]").replace("'", "").replace(" ", "").split(",")
-
-            # Show a warning if more than one item is present in the list (this means we are using the old method)
-            logger.critical(
-                "DeprecationWarning: Please use the new method of providing context names, "
-                "i.e. --output-filetypes 'type1 type2 type3'."
-            )
-            logger.critical(
-                "If you are using COMO, this can be done by setting the 'context_names' variable to a "
-                "simple string separated by spaces. Here are a few examples!"
-            )
-            logger.critical("context_names = 'cellType1 cellType2 cellType3'")
-            logger.critical("output_filetypes = 'output1 output2 output3'")
-            logger.critical(
-                "\nYour current method of passing context names will be removed in the future. "
-                "Update your variables above accordingly!\n\n"
-            )
-
-        else:
-            new_list: list[str] = stringlist.split(" ")
-
-        return new_list
-    return stringlist
+    if isinstance(stringlist, list):
+        return stringlist
+
+    if not (stringlist.startswith("[") and stringlist.endswith("]")):
+        return stringlist.split(" ")
+
+    # Remove any brackets from the first and last items; replace quotation marks and commas with nothing
+    new_list: list[str] = stringlist.strip("[]").replace("'", "").replace(" ", "").split(",")
+
+    # Show a warning if more than one item is present in the list (this means we are using the old method)
+    logger.critical(
+        "DeprecationWarning: Please use the new method of providing context names, "
+        "i.e. --output-filetypes 'type1 type2 type3'."
+    )
+    logger.critical(
+        "If you are using COMO, this can be done by setting the 'context_names' variable to a "
+        "simple string separated by spaces. Here are a few examples!"
+    )
+    logger.critical("context_names = 'cellType1 cellType2 cellType3'")
+    logger.critical("output_filetypes = 'output1 output2 output3'")
+    logger.critical(
+        "\nYour current method of passing context names will be removed in the future. "
+        "Update your variables above accordingly!\n\n"
+    )
+
+    return new_list
 
 
 def split_gene_expression_data(expression_data: pd.DataFrame, recon_algorithm: Algorithm | None = None):
@@ -258,3 +258,8 @@ async def convert_gene_data(values: list[str], taxon_id: int | str | Taxon) -> p
         return await gene_id_to_ensembl_and_gene_symbol(ids=values, taxon=taxon_id)
     else:
         raise ValueError("Gene data must be of the same type (i.e., all Ensembl, Entrez, or Gene Symbols)")
+
+
+def _listify(value):
+    """Convert items into a list."""
+    return [value] if not isinstance(value, list) else value

From 90c43f35e3ed5dbaf1a67f378d33e6f6f7db3131 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 5 Dec 2024 16:18:59 +0000
Subject: [PATCH 002/345] chore(deps): bump astral-sh/setup-uv from 3 to 4

Bumps [astral-sh/setup-uv](https://github.com/astral-sh/setup-uv) from 3 to 4.
- [Release notes](https://github.com/astral-sh/setup-uv/releases)
- [Commits](https://github.com/astral-sh/setup-uv/compare/v3...v4)

---
updated-dependencies:
- dependency-name: astral-sh/setup-uv
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 .github/workflows/continuous_integration.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/continuous_integration.yml b/.github/workflows/continuous_integration.yml
index 8109f213..7bd2c871 100644
--- a/.github/workflows/continuous_integration.yml
+++ b/.github/workflows/continuous_integration.yml
@@ -14,7 +14,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v3
+        uses: astral-sh/setup-uv@v4
 
       - name: Create Virtual Environment
         run: uv venv

From bfcafa3cba8815ab932266148bb5bf3a5e509cb0 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 5 Dec 2024 16:19:03 +0000
Subject: [PATCH 003/345] chore(deps): bump astral-sh/ruff-action from 1 to 2

Bumps [astral-sh/ruff-action](https://github.com/astral-sh/ruff-action) from 1 to 2.
- [Release notes](https://github.com/astral-sh/ruff-action/releases)
- [Changelog](https://github.com/astral-sh/ruff-action/blob/main/release-drafter.yml)
- [Commits](https://github.com/astral-sh/ruff-action/compare/v1...v2)

---
updated-dependencies:
- dependency-name: astral-sh/ruff-action
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
---
 .github/workflows/continuous_integration.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/continuous_integration.yml b/.github/workflows/continuous_integration.yml
index 8109f213..869f587d 100644
--- a/.github/workflows/continuous_integration.yml
+++ b/.github/workflows/continuous_integration.yml
@@ -26,17 +26,17 @@ jobs:
         run: uv run jupyter nbconvert --clear-output --inplace "main/COMO.ipynb"
 
       - name: Format Python Imports
-        uses: astral-sh/ruff-action@v1
+        uses: astral-sh/ruff-action@v2
         with:
           args: "check --fix --select I"
 
       - name: Format code
-        uses: astral-sh/ruff-action@v1
+        uses: astral-sh/ruff-action@v2
         with:
           args: "format"
 
       - name: Format Notebook
-        uses: astral-sh/ruff-action@v1
+        uses: astral-sh/ruff-action@v2
         with:
           args: "format main/COMO.ipynb"
 
@@ -54,7 +54,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Check Lint
-        uses: astral-sh/ruff-action@v1
+        uses: astral-sh/ruff-action@v2
         with:
           args: "check --no-fix --verbose"
 

From 3e81a3de65cd40d2db137b95c4275e7da14fc9f4 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 6 Dec 2024 23:14:57 -0600
Subject: [PATCH 004/345] refactor: allow formatting of jupyter notebooks

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 ruff.toml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ruff.toml b/ruff.toml
index 9591bda3..455091e3 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -1,6 +1,6 @@
 line-length = 120
-extend-include = ["docs/**/*.py", "tests/**/*.py"]
-exclude = ["__init__.py", "main/COMO.ipynb"]
+extend-include = ["docs/**/*.py", "tests/**/*.py", "**/*.ipynb"]
+exclude = ["__init__.py"]
 
 [format]
 quote-style = "double"

From 476ca849247a1eb2b954dff0f2bea39ed4033286 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 6 Dec 2024 23:37:01 -0600
Subject: [PATCH 005/345] refactor: rename

---
 main/como/custom_types.py | 32 -------------------------
 main/como/types.py        | 49 +++++++++++++++++++++++++++++++++++++++
 2 files changed, 49 insertions(+), 32 deletions(-)
 delete mode 100644 main/como/custom_types.py
 create mode 100644 main/como/types.py

diff --git a/main/como/custom_types.py b/main/como/custom_types.py
deleted file mode 100644
index 0f464203..00000000
--- a/main/como/custom_types.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from __future__ import annotations
-
-from enum import Enum
-from pathlib import Path
-
-from fast_bioservices import Taxon
-
-
-class RNASeqPreparationMethod(Enum):
-    TOTAL = "total"
-    MRNA = "mrna"
-    SCRNA = "scrna"
-
-    @staticmethod
-    def from_string(value: str) -> RNASeqPreparationMethod:
-        """Build a preparation method object from a string."""
-        match_value = "".join(c for c in value if c.isascii()).lower()
-
-        match match_value:
-            case "total" | "trna":
-                return RNASeqPreparationMethod.TOTAL
-            case "mrna":
-                return RNASeqPreparationMethod.MRNA
-            case "scrna":
-                return RNASeqPreparationMethod.SCRNA
-            case _:
-                possible_values = [t.value for t in RNASeqPreparationMethod]
-                raise ValueError(f"Filtering technique must be one of {possible_values}; got: {value}")
-
-
-type_taxon = Taxon | int | str
-type_path = str | Path
diff --git a/main/como/types.py b/main/como/types.py
new file mode 100644
index 00000000..8e657f7f
--- /dev/null
+++ b/main/como/types.py
@@ -0,0 +1,49 @@
+from __future__ import annotations
+
+from abc import ABC
+from enum import Enum
+from pathlib import Path
+
+from pydantic import BaseModel, ConfigDict
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+
+class _BaseModel(BaseModel, ABC):
+    model_config = ConfigDict()
+
+
+class _BaseArguments(BaseSettings, ABC):
+    model_config = SettingsConfigDict(
+        cli_parse_args=True,
+        cli_kebab_case=True,
+        nested_model_default_partial_update=True,
+        case_sensitive=True,
+        cli_avoid_json=True,
+        cli_enforce_required=True,
+    )
+
+
+class RNAPrepMethod(Enum):
+    TOTAL = "total"
+    MRNA = "mrna"
+    SCRNA = "scrna"
+
+    @staticmethod
+    def from_string(value: str) -> RNAPrepMethod:
+        """Build a preparation method object from a string."""
+        match_value = "".join(c for c in value if c.isascii()).lower()
+
+        match match_value:
+            case "total" | "trna":
+                return RNAPrepMethod.TOTAL
+            case "mrna":
+                return RNAPrepMethod.MRNA
+            case "scrna":
+                return RNAPrepMethod.SCRNA
+            case _:
+                possible_values = [t.value for t in RNAPrepMethod]
+                raise ValueError(f"Filtering technique must be one of {possible_values}; got: {value}")
+
+
+type_path = str | Path
+type_rna = Literal["total", "polya"]

From 97160e705f34de1801ede4d147acdad2ee730a49 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sat, 7 Dec 2024 00:28:42 -0600
Subject: [PATCH 006/345] refactor: import required libraries

---
 main/como/rnaseq_preprocess.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index ee02948c..67092955 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -1,23 +1,23 @@
 from __future__ import annotations
 
-import argparse
 import asyncio
+import contextlib
 import re
+import sys
 from dataclasses import dataclass, field
-from io import StringIO
+from io import StringIO, TextIOWrapper
+from itertools import chain
 from pathlib import Path
-from typing import Literal, NamedTuple
+from typing import Literal
 
 import aiofiles
 import numpy as np
 import pandas as pd
 import scanpy as sc
 from fast_bioservices.biothings.mygene import MyGene
+from fast_bioservices.pipeline import ensembl_to_gene_id_and_symbol, gene_symbol_to_ensembl_and_gene_id
 from loguru import logger
 
-from como.custom_types import type_path, type_taxon
-from como.utils import _listify, convert_gene_data
-
 type_rna = Literal["total", "mrna"]
 
 
@@ -34,6 +34,7 @@ class _Arguments(NamedTuple):
     output_trna_count_matrix: list[Path] | None
     output_mrna_count_matrix: list[Path] | None
     cache: bool
+from como.types import RNAPrepMethod, type_path, type_rna
 
 
 @dataclass

From 0cacd94c29d523af2dbc6e7b0d26646e606fb020 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sat, 7 Dec 2024 00:29:26 -0600
Subject: [PATCH 007/345] refactor: remove unused arguments dataclass

---
 main/como/rnaseq_preprocess.py | 17 +----------------
 1 file changed, 1 insertion(+), 16 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 67092955..43ddc42c 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -18,23 +18,8 @@
 from fast_bioservices.pipeline import ensembl_to_gene_id_and_symbol, gene_symbol_to_ensembl_and_gene_id
 from loguru import logger
 
-type_rna = Literal["total", "mrna"]
-
-
-class _Arguments(NamedTuple):
-    context_names: list[str]
-    mode: list[Literal["create", "provide"]]
-    taxon_id: list[str]
-    input_como_dirpath: list[Path] | None
-    input_matrix_filepath: list[Path] | None
-    output_gene_info_filepath: Path | None
-    output_count_matrices_dir: list[Path] | None
-    output_trna_config_filepath: Path | None
-    output_mrna_config_filepath: Path | None
-    output_trna_count_matrix: list[Path] | None
-    output_mrna_count_matrix: list[Path] | None
-    cache: bool
 from como.types import RNAPrepMethod, type_path, type_rna
+from como.utils import _listify
 
 
 @dataclass

From 9b45c1d5925d0acff0cf62c5c89a826b9aff44ac Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sat, 7 Dec 2024 00:51:20 -0600
Subject: [PATCH 008/345] refactor: remove unused cli argument parsing

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 43ddc42c..f4231d5b 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -32,7 +32,7 @@ class _STARinformation:
     count_matrix: pd.DataFrame
 
     @property
-    def num_genes(self):
+    def num_genes(self) -> int:
         return len(self.count_matrix)
 
     @classmethod
@@ -120,8 +120,8 @@ def _sample_name_from_filepath(file: Path) -> str:
 
 
 def _organize_gene_counts_files(data_dir: Path) -> list[_StudyMetrics]:
-    gene_count_dir = Path(data_dir, "geneCounts").resolve()
-    strand_dir = Path(data_dir, "strandedness").resolve()
+    gene_count_dir = Path(data_dir, "geneCounts")
+    strand_dir = Path(data_dir, "strandedness")
 
     gene_counts_directories: list[Path] = sorted([p for p in gene_count_dir.glob("*") if not p.name.startswith(".")])
     strandedness_directories: list[Path] = sorted([p for p in strand_dir.glob("*") if not p.name.startswith(".")])

From 31f59a7039c97a4cf9260647e8bd8fde859fd83e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sat, 7 Dec 2024 00:54:13 -0600
Subject: [PATCH 009/345] fix: get correct column name

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index f4231d5b..687af089 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -166,7 +166,7 @@ async def _process_first_multirun_sample(strand_file: Path, all_counts_files: li
         if strand_information == "none":
             strand_information = "unstranded_rna_counts"
 
-        run_counts = star_information.count_matrix[["gene_id", strand_information]]
+        run_counts = star_information.count_matrix[["ensembl_gene_id", strand_information]]
         run_counts.columns = pd.Index(["ensembl_gene_id", "counts"])
         if sample_count.empty:
             sample_count = run_counts

From 8462ab7d00a5295d2091e273425d66a446d9f7ab Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sat, 7 Dec 2024 00:54:38 -0600
Subject: [PATCH 010/345] refactor: pythonic approach to merging dataframes

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 687af089..87417020 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -168,11 +168,11 @@ async def _process_first_multirun_sample(strand_file: Path, all_counts_files: li
 
         run_counts = star_information.count_matrix[["ensembl_gene_id", strand_information]]
         run_counts.columns = pd.Index(["ensembl_gene_id", "counts"])
-        if sample_count.empty:
-            sample_count = run_counts
-        else:
-            # Merge to take all items from both data frames
-            sample_count = sample_count.merge(run_counts, on="ensembl_gene_id", how="outer")
+        sample_count = (
+            run_counts
+            if sample_count.empty
+            else sample_count.merge(run_counts, on=["ensembl_gene_id", "counts"], how="outer")
+        )
 
     # Set na values to 0
     sample_count = sample_count.fillna(value="0")

From caea939355a1391d12b102def9e8834f0a07e343 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sat, 7 Dec 2024 01:02:33 -0600
Subject: [PATCH 011/345] style: variable renaming

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/merge_xomics.py      | 10 +++++-----
 main/como/rnaseq_preprocess.py |  6 +++---
 main/como/types.py             |  1 +
 3 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 7079d456..3111ec66 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -16,7 +16,7 @@
 
 from como import proteomics_gen, return_placeholder_data
 from como.combine_distributions import _combine_zscores
-from como.custom_types import RNASeqPreparationMethod
+from como.custom_types import RNAPrepMethod
 from como.project import Config
 from como.utils import split_gene_expression_data
 
@@ -93,7 +93,7 @@ def __post_init__(self):
             raise ValueError("Adjust method must be either 'progressive', 'regressive', 'flat', or 'custom'")
 
 
-def _load_rnaseq_tests(filename, context_name, prep_method: RNASeqPreparationMethod) -> tuple[str, pd.DataFrame]:
+def _load_rnaseq_tests(filename, context_name, prep_method: RNAPrepMethod) -> tuple[str, pd.DataFrame]:
     """Load rnaseq results.
 
     Returns a dictionary of test (context, context, cell, etc ) names and rnaseq expression data
@@ -112,11 +112,11 @@ def load_dummy_dict():
         raise FileNotFoundError(f"Error: Config file not found at {inquiry_full_path}")
 
     match prep_method:
-        case RNASeqPreparationMethod.TOTAL:
+        case RNAPrepMethod.TOTAL:
             filename = f"rnaseq_total_{context_name}.csv"
-        case RNASeqPreparationMethod.MRNA:
+        case RNAPrepMethod.MRNA:
             filename = f"rnaseq_mrna_{context_name}.csv"
-        case RNASeqPreparationMethod.SCRNA:
+        case RNAPrepMethod.SCRNA:
             filename = f"rnaseq_scrna_{context_name}.csv"
         case _:
             raise ValueError(
diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 87417020..3d31e050 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -253,7 +253,7 @@ async def _write_counts_matrix(
     config_df: pd.DataFrame,
     como_context_dir: Path,
     output_counts_matrix_filepath: Path,
-    rna_type: type_rna,
+    rna: type_rna,
 ) -> pd.DataFrame:
     """Create a counts matrix file by reading gene counts table(s)."""
     study_metrics = _organize_gene_counts_files(data_dir=como_context_dir)
@@ -264,12 +264,12 @@ async def _write_counts_matrix(
     for count in counts:
         final_matrix = count if final_matrix.empty else pd.merge(final_matrix, count, on="ensembl_gene_id", how="outer")
 
-    rna_specific_sample_names = config_df.loc[config_df["library_prep"] == rna_type, "sample_name"].tolist()
+    rna_specific_sample_names = config_df.loc[config_df["library_prep"] == rna, "sample_name"].tolist()
     final_matrix = final_matrix[["ensembl_gene_id", *rna_specific_sample_names]]
 
     output_counts_matrix_filepath.parent.mkdir(parents=True, exist_ok=True)
     final_matrix.to_csv(output_counts_matrix_filepath, index=False)
-    logger.success(f"Wrote gene count matrix at '{output_counts_matrix_filepath}'")
+    logger.success(f"Wrote gene count matrix for '{rna}' RNA at '{output_counts_matrix_filepath}'")
     return final_matrix
 
 
diff --git a/main/como/types.py b/main/como/types.py
index 8e657f7f..ebe44ecf 100644
--- a/main/como/types.py
+++ b/main/como/types.py
@@ -3,6 +3,7 @@
 from abc import ABC
 from enum import Enum
 from pathlib import Path
+from typing import Literal
 
 from pydantic import BaseModel, ConfigDict
 from pydantic_settings import BaseSettings, SettingsConfigDict

From 43e1a2e0202c4d923c2c96adf4198b0d4640a68e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sat, 7 Dec 2024 01:02:47 -0600
Subject: [PATCH 012/345] style: ruff formatting

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/merge_xomics.py | 12 +++---------
 1 file changed, 3 insertions(+), 9 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 3111ec66..8dc2a6fd 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -344,15 +344,9 @@ async def _merge_xomics(
     config = Config()
     logger.info(f"Merging data for {context_name}")
     # load data for each source if it exists. IF not load an empty dummy dataset
-    trnaseq = _load_rnaseq_tests(
-        filename=trnaseq_file, context_name=context_name, prep_method=RNASeqPreparationMethod.TOTAL
-    )
-    mrnaseq = _load_rnaseq_tests(
-        filename=mrnaseq_file, context_name=context_name, prep_method=RNASeqPreparationMethod.MRNA
-    )
-    scrnaseq = _load_rnaseq_tests(
-        filename=scrnaseq_file, context_name=context_name, prep_method=RNASeqPreparationMethod.SCRNA
-    )
+    trnaseq = _load_rnaseq_tests(filename=trnaseq_file, context_name=context_name, prep_method=RNAPrepMethod.TOTAL)
+    mrnaseq = _load_rnaseq_tests(filename=mrnaseq_file, context_name=context_name, prep_method=RNAPrepMethod.MRNA)
+    scrnaseq = _load_rnaseq_tests(filename=scrnaseq_file, context_name=context_name, prep_method=RNAPrepMethod.SCRNA)
     proteomics = proteomics_gen.load_proteomics_tests(filename=proteomics_file, context_name=context_name)
 
     expression_list = []

From 963d4816cdf791d4bc14e98be983c6e2ff753283 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sat, 7 Dec 2024 01:46:32 -0600
Subject: [PATCH 013/345] =?UTF-8?q?=E2=9C=A8=20feat:=20added=20gitmoji?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .pre-commit-config.yaml | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 16d682c0..d72921b9 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,8 +1,11 @@
-# See https://pre-commit.com for more information
-# See https://pre-commit.com/hooks.html for more hooks
 repos:
--   repo: https://github.com/opensource-nepal/commitlint
-    rev: v1.2.0
+  - repo: https://github.com/ljnsn/cz-conventional-gitmoji
+    rev: v0.6.1
     hooks:
-    -   id: commitlint
-        name: Commit Lint
+      - id: conventional-gitmoji
+#  - repo: https://github.com/commitizen-tools/commitizen
+#    rev: v3.29.0
+#    hooks:
+#      - id: commitizen
+#        additional_dependencies: [cz-conventional-gitmoji]
+#        stages: [commit-msg]

From c389fd9e8531737afa70e059b3c65c0b0aa56605 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sat, 7 Dec 2024 01:48:52 -0600
Subject: [PATCH 014/345] =?UTF-8?q?=E2=99=BB=EF=B8=8F=20refactor:=20remove?=
 =?UTF-8?q?=20commitizen?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .pre-commit-config.yaml | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index d72921b9..8a1635ef 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -3,9 +3,3 @@ repos:
     rev: v0.6.1
     hooks:
       - id: conventional-gitmoji
-#  - repo: https://github.com/commitizen-tools/commitizen
-#    rev: v3.29.0
-#    hooks:
-#      - id: commitizen
-#        additional_dependencies: [cz-conventional-gitmoji]
-#        stages: [commit-msg]

From 993b4a8651e25adb5e750122fe6a1a1b3a76b662 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sat, 7 Dec 2024 01:52:00 -0600
Subject: [PATCH 015/345] =?UTF-8?q?=F0=9F=8E=A8=20style:=20change=20variab?=
 =?UTF-8?q?le=20names?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 main/como/rnaseq.py     | 10 +++++-----
 main/como/rnaseq_gen.py | 14 +++++++-------
 2 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/main/como/rnaseq.py b/main/como/rnaseq.py
index 1c504d97..d67658e6 100644
--- a/main/como/rnaseq.py
+++ b/main/como/rnaseq.py
@@ -27,7 +27,7 @@
 from scipy.signal import find_peaks
 from sklearn.neighbors import KernelDensity
 
-from como.custom_types import RNASeqPreparationMethod
+from como.custom_types import RNAPrepMethod
 from como.migrations import gene_info_migrations
 from como.project import Config
 from como.utils import convert_gene_data
@@ -528,7 +528,7 @@ def cpm_filter(
     context_name: str,
     metrics: NamedMetrics,
     filtering_options: _FilteringOptions,
-    prep: RNASeqPreparationMethod,
+    prep: RNAPrepMethod,
 ) -> NamedMetrics:
     """Apply Counts Per Million (CPM) filtering to the count matrix for a given sample."""
     config = Config()
@@ -660,7 +660,7 @@ def filter_counts(
     metrics: NamedMetrics,
     technique: FilteringTechnique,
     filtering_options: _FilteringOptions,
-    prep: RNASeqPreparationMethod,
+    prep: RNAPrepMethod,
 ) -> NamedMetrics:
     """Filter the count matrix based on the specified technique."""
     match technique:
@@ -684,7 +684,7 @@ async def save_rnaseq_tests(
     config_filepath: Path,
     gene_info_filepath: Path,
     output_filepath: Path,
-    prep: RNASeqPreparationMethod,
+    prep: RNAPrepMethod,
     taxon_id: Taxon,
     replicate_ratio: float,
     batch_ratio: float,
@@ -702,7 +702,7 @@ async def save_rnaseq_tests(
         high_batch_ratio=high_batch_ratio,
     )
 
-    if prep == RNASeqPreparationMethod.SCRNA:
+    if prep == RNAPrepMethod.SCRNA:
         technique = FilteringTechnique.umi
         logger.warning(
             "Single cell filtration does not normalize and assumes "
diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 8f8ffa34..47aeb8ee 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -9,7 +9,7 @@
 from loguru import logger
 
 from como import Config
-from como.custom_types import RNASeqPreparationMethod
+from como.custom_types import RNAPrepMethod
 from como.rnaseq import FilteringTechnique, save_rnaseq_tests
 
 
@@ -22,11 +22,11 @@ class _Arguments:
     high_batch_ratio: float
     filtering_technique: FilteringTechnique
     minimum_cutoff: int | str
-    library_prep: RNASeqPreparationMethod
+    library_prep: RNAPrepMethod
     taxon: Taxon
 
     def __post_init__(self):
-        self.library_prep = RNASeqPreparationMethod.from_string(str(self.library_prep))
+        self.library_prep = RNAPrepMethod.from_string(str(self.library_prep))
         self.filtering_technique = FilteringTechnique.from_string(str(self.filtering_technique))
 
         if self.minimum_cutoff is None:
@@ -46,7 +46,7 @@ async def _handle_context_batch(
     batch_ratio_high: float,
     technique: FilteringTechnique,
     cut_off: int | float | str,
-    prep: RNASeqPreparationMethod,
+    prep: RNAPrepMethod,
     taxon: Taxon,
 ) -> None:
     """Iterate through each context type and create rnaseq expression file.
@@ -81,9 +81,9 @@ async def _handle_context_batch(
         rnaseq_input_filepath = (
             config.data_dir / "data_matrices" / context_name / f"gene_counts_matrix_{prep.value}_{context_name}"
         )
-        if prep == RNASeqPreparationMethod.SCRNA:
+        if prep == RNAPrepMethod.SCRNA:
             rnaseq_input_filepath = rnaseq_input_filepath.with_suffix(".h5ad")
-        elif prep in {RNASeqPreparationMethod.TOTAL, RNASeqPreparationMethod.MRNA}:
+        elif prep in {RNAPrepMethod.TOTAL, RNAPrepMethod.MRNA}:
             rnaseq_input_filepath = rnaseq_input_filepath.with_suffix(".csv")
 
         if not rnaseq_input_filepath.exists():
@@ -117,7 +117,7 @@ async def _handle_context_batch(
 async def rnaseq_gen(
     # config_filepath: Path,
     config_filename: str,
-    prep: RNASeqPreparationMethod,
+    prep: RNAPrepMethod,
     taxon_id: int | str | Taxon,
     replicate_ratio: float = 0.5,
     high_replicate_ratio: float = 1.0,

From c4f53bc75a83392fb4f76a58b49771bc13b95fd7 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sat, 7 Dec 2024 01:52:37 -0600
Subject: [PATCH 016/345] =?UTF-8?q?=F0=9F=8E=A8=20style:=20sort=20dependen?=
 =?UTF-8?q?cies?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 pyproject.toml | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 2c6d42c5..8e271ec0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,22 +3,24 @@ name = "COMO"
 dynamic = ["version"]
 requires-python = ">=3.10,<3.13"
 dependencies = [
+    "aiofiles>=24.1.0",
+    "aioftp>=0.23.1",
+    "cobamp",
     "cobra>=0.28.0",
     "fast-bioservices>=0.3.9",
     "gurobipy>=11.0",
     "kaleido==0.2.1",
     "loguru>=0.7.2",
+    "openpyxl>=3.1.5",
     "pandas>=1.3.5",
     "plotly>=5.24.1",
+    "pydantic-settings",
+    "pydantic>=2.10.3",
     "scanpy>=1.9.8",
-    "scipy>=1.7.3",
     "scikit-learn>=1.5.2",
+    "scipy>=1.7.3",
     "setuptools<60.0",
-    "openpyxl>=3.1.5",
-    "aiofiles>=24.1.0",
-    "aioftp>=0.23.1",
     "troppo",
-    "cobamp",
 ]
 
 [build-system]

From 9dd5de6aef8487326249d4ea550d85c4102010eb Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sat, 7 Dec 2024 01:54:00 -0600
Subject: [PATCH 017/345] =?UTF-8?q?=E2=9E=95=20dep-add:=20add=20interactiv?=
 =?UTF-8?q?e=20optional=20dependencies?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 pyproject.toml | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index 8e271ec0..4519ce67 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,6 +23,12 @@ dependencies = [
     "troppo",
 ]
 
+[project.optional-dependencies]
+interactive = [
+    "ipython>=8.0.0",
+    "jupyterlab>=4.3.2",
+]
+
 [build-system]
 requires = ["hatchling"]
 build-backend = "hatchling.build"

From ba0c445003e69e345f9f1a1504ddaec36c6f0369 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sat, 7 Dec 2024 01:54:33 -0600
Subject: [PATCH 018/345] =?UTF-8?q?=E2=9E=95=20dep-add:=20add=20commitizen?=
 =?UTF-8?q?=20and=20conventional=20gitmoji?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 pyproject.toml | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index 4519ce67..859ddcbe 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -53,8 +53,12 @@ dev-dependencies = [
     "ruff>=0.8.0",
     "hypothesis>=6.122.1",
     "pytest-cov>=6.0.0",
+    "commitizen>=4.1.0",
+    "cz-conventional-gitmoji>=0.6.1",
 ]
 
 [tool.uv.sources]
 troppo = { git = "https://github.com/JoshLoecker/troppo", rev = "update_dependencies" }
 cobamp = { git = "https://github.com/JoshLoecker/cobamp", rev = "update_packages" }
+pydantic-settings = { git = "https://github.com/pydantic/pydantic-settings" }
+

From c7d0576e2138fea281c330d3ae7d994a012e79b4 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 10:48:43 -0600
Subject: [PATCH 019/345] =?UTF-8?q?=E2=99=BB=EF=B8=8F=20refactor:=20accept?=
 =?UTF-8?q?=20file=20paths=20instead=20of=20dataframes?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 main/como/rnaseq_preprocess.py | 20 +++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 3d31e050..c6cfd271 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -427,7 +427,7 @@ async def _create_config_df(context_name: str, /, como_input_dir: Path) -> pd.Da
 
 async def _create_gene_info_file(
     *,
-    counts_matrix: pd.DataFrame | Path,
+    counts_matrix_filepaths: list[Path],
     output_filepath: Path,
     taxon_id: type_taxon,
     cache: bool,
@@ -435,15 +435,17 @@ async def _create_gene_info_file(
     """Create gene info file for specified context by reading first column in its count matrix file."""
     logger.info("Fetching gene info")
 
-    data: pd.DataFrame | sc.AnnData = (
-        (pd.read_csv(counts_matrix) if counts_matrix.suffix == ".csv" else sc.read_h5ad(counts_matrix))
-        if isinstance(counts_matrix, Path)
-        else counts_matrix
-    )
+    async def read_counts(file: Path) -> list[str]:
+        data = await asyncio.to_thread(pd.read_csv if file.suffix == ".csv" else sc.read_h5ad, file)
+        conversion = (
+            await ensembl_to_gene_id_and_symbol(ids=data["ensembl_gene_id"].tolist(), taxon=taxon)
+            if isinstance(data, pd.DataFrame)
+            else await gene_symbol_to_ensembl_and_gene_id(symbols=data.var_names.tolist(), taxon=taxon)
+        )
+        return conversion["entrez_gene_id"].tolist()
 
-    input_values = data.iloc[:, 0].tolist() if isinstance(data, pd.DataFrame) else data.var_names.tolist()
-    conversion = await convert_gene_data(input_values, taxon_id)
-    genes = conversion["entrez_gene_id"].astype(str).tolist()
+    logger.info("Fetching gene info (this may take 1-5 minutes)")
+    genes = set(chain.from_iterable(await asyncio.gather(*[read_counts(f) for f in counts_matrix_filepaths])))
 
     mygene = MyGene(cache=cache)
     gene_data = await mygene.query(

From b18383a00bdbc915b8553e462bc31cba4cde9cfc Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 10:49:34 -0600
Subject: [PATCH 020/345] =?UTF-8?q?=F0=9F=93=9D=20docs:=20update=20documen?=
 =?UTF-8?q?tation?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 main/como/rnaseq_preprocess.py | 23 +++++++++++++----------
 1 file changed, 13 insertions(+), 10 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index c6cfd271..58122de3 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -432,8 +432,10 @@ async def _create_gene_info_file(
     taxon_id: type_taxon,
     cache: bool,
 ):
-    """Create gene info file for specified context by reading first column in its count matrix file."""
-    logger.info("Fetching gene info")
+    """Create a gene information file context.
+
+    The gene information file will be created by reading each matrix filepath in the provided list
+    """
 
     async def read_counts(file: Path) -> list[str]:
         data = await asyncio.to_thread(pd.read_csv if file.suffix == ".csv" else sc.read_h5ad, file)
@@ -616,19 +618,20 @@ async def rnaseq_preprocess(  # noqa: C901
     Fetches additional gene information from a provided matrix or gene counts,
         or optionally creates this matrix using gene count files obtained using STAR aligner
 
-    :param context_names: The context/cell type being processed
-    :param mode: The mode of operation
-    :param taxon_id: The NCBI taxonomy ID
+    :param context_name: The context/cell type being processed
+    :param taxon: The NCBI taxonomy ID
     :param output_gene_info_filepath: Path to the output gene information CSV file
     :param output_trna_config_filepath: Path to the output tRNA config file (if in "create" mode)
-    :param output_mrna_config_filepath: Path to the output mRNA config file (if in "create" mode)
-    :param output_count_matrices_dirpath: The path to write all created count matrices
-    :param output_trna_count_matrix: The path to write total RNA count matrices
-    :param output_mrna_count_matrix: The path to write messenger RNA count matrices
-    :param input_como_dirpath: If in "create" mode, the input path(s) to the COMO_input directory of the current context
+    :param output_polya_config_filepath: Path to the output mRNA config file (if in "create" mode)
+    :param output_trna_count_matrix_filepath: The path to write total RNA count matrices
+    :param output_polya_count_matrix_filepath: The path to write messenger RNA count matrices
+    :param como_context_dir: If in "create" mode, the input path(s) to the COMO_input directory of the current context
         i.e., the directory containing "fragmentSizes", "geneCounts", "insertSizeMetrics", etc. directories
     :param input_matrix_filepath: If in "provide" mode, the path(s) to the count matrices to be processed
+    :param preparation_method: The preparation method
     :param cache: Should HTTP requests be cached
+    :param log_level: The logging level
+    :param log_location: The logging location
     """
     context_names = _listify(context_names)
     mode = _listify(mode)

From 90a6dac1e9058c3929ce96accad476dc252d8daf Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 10:50:18 -0600
Subject: [PATCH 021/345] =?UTF-8?q?=E2=99=BB=EF=B8=8F=20refactor:=20accept?=
 =?UTF-8?q?=20integers=20only=20for=20taxon=20IDs?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 main/como/rnaseq_preprocess.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 58122de3..48bdd3b3 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -450,11 +450,7 @@ async def read_counts(file: Path) -> list[str]:
     genes = set(chain.from_iterable(await asyncio.gather(*[read_counts(f) for f in counts_matrix_filepaths])))
 
     mygene = MyGene(cache=cache)
-    gene_data = await mygene.query(
-        items=genes,
-        taxon=taxon_id,
-        scopes="entrezgene",
-    )
+    gene_data = await mygene.query(items=list(genes), taxon=taxon, scopes="entrezgene")
     gene_info: pd.DataFrame = pd.DataFrame(
         data=None,
         columns=pd.Index(data=["ensembl_gene_id", "gene_symbol", "entrez_gene_id", "start_position", "end_position"]),

From e321c3e1dc0cc7434b7437d17da6a7b638adca76 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 10:50:44 -0600
Subject: [PATCH 022/345] =?UTF-8?q?=E2=99=BB=EF=B8=8F=20refactor:=20python?=
 =?UTF-8?q?ic=20approach=20to=20calculating=20size?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 main/como/rnaseq_preprocess.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 48bdd3b3..e83ab0e8 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -462,11 +462,9 @@ async def read_counts(file: Path) -> list[str]:
             ensembl_ids = ensembl_ids[0]
 
         start_pos = data.get("genomic_pos.start", 0)
+        start_pos = sum(start_pos) / len(start_pos) if isinstance(start_pos, list) else start_pos
         end_pos = data.get("genomic_pos.end", 0)
-        if isinstance(start_pos, list):
-            start_pos = sum(start_pos) / len(start_pos)
-        if isinstance(end_pos, list):
-            end_pos = sum(end_pos) / len(end_pos)
+        end_pos = sum(end_pos) / len(end_pos) if isinstance(end_pos, list) else end_pos
 
         gene_info.at[i, "gene_symbol"] = data.get("symbol", "-")
         gene_info.at[i, "entrez_gene_id"] = data.get("entrezgene", "-")

From e54309e260146c0919ad92d80a72715629765951 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 10:51:18 -0600
Subject: [PATCH 023/345] =?UTF-8?q?=E2=99=BB=EF=B8=8F=20refactor:=20accept?=
 =?UTF-8?q?=20integer=20for=20taxon=20id?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 main/como/rnaseq_preprocess.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index e83ab0e8..9df07cf4 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -429,7 +429,7 @@ async def _create_gene_info_file(
     *,
     counts_matrix_filepaths: list[Path],
     output_filepath: Path,
-    taxon_id: type_taxon,
+    taxon: int,
     cache: bool,
 ):
     """Create a gene information file context.
@@ -473,10 +473,15 @@ async def read_counts(file: Path) -> list[str]:
         gene_info.at[i, "end_position"] = end_pos
 
     gene_info = gene_info[
-        (gene_info["entrez_gene_id"] != "-") & (gene_info["ensembl_gene_id"] != "-") & (gene_info["gene_symbol"] != "-")
+        (
+            (gene_info["entrez_gene_id"] != "-")
+            & (gene_info["ensembl_gene_id"] != "-")
+            & (gene_info["gene_symbol"] != "-")
+        )
     ]
     gene_info["size"] = gene_info["end_position"].astype(int) - gene_info["start_position"].astype(int)
     gene_info.drop(columns=["start_position", "end_position"], inplace=True)
+    gene_info.sort_values(by="ensembl_gene_id", inplace=True)
     gene_info.to_csv(output_filepath, index=False)
     logger.success(f"Gene Info file written at '{output_filepath}'")
 

From 6cc638f3b09e75667883b8fe0d925dffaaa2d3ae Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 10:52:11 -0600
Subject: [PATCH 024/345] =?UTF-8?q?=E2=99=BB=EF=B8=8F=20refactor:=20more?=
 =?UTF-8?q?=20concise=20method=20of=20creating=20matrix?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 main/como/rnaseq_preprocess.py | 33 ++++++++-------------------------
 1 file changed, 8 insertions(+), 25 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 9df07cf4..ade8e4d7 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -488,40 +488,23 @@ async def read_counts(file: Path) -> list[str]:
 
 async def _create_matrix_file(
     context_name: str,
-    taxon_id: type_taxon,
-    output_gene_info_filepath: Path,
     output_config_filepath: Path,
-    output_gene_matrix_filepath: Path,
-    como_dirpath: type_path,
-    rna_type: type_rna,
-    cache: bool,
+    como_context_dir: type_path,
+    output_counts_matrix_filepath: Path,
+    rna: type_rna,
 ) -> None:
-    como_context_dir = como_dirpath / context_name
-    config_df = await _create_config_df(context_name, como_input_dir=como_dirpath)
-    counts_matrix = await _write_counts_matrix(
+    config_df = await _create_config_df(context_name, como_input_dir=como_context_dir)
+    await _write_counts_matrix(
         config_df=config_df,
         como_context_dir=como_context_dir,
-        output_counts_matrix_filepath=output_gene_matrix_filepath,
-        rna_type=rna_type,
+        output_counts_matrix_filepath=output_counts_matrix_filepath,
+        rna=rna,
     )
     with pd.ExcelWriter(output_config_filepath) as writer:
-        subset_config = config_df[config_df["library_prep"] == rna_type]
+        subset_config = config_df[config_df["library_prep"] == rna]
         subset_config.to_excel(writer, sheet_name=context_name, header=True, index=False)
 
-    await _create_gene_info_file(
-        counts_matrix=counts_matrix,
-        output_filepath=output_gene_info_filepath,
-        taxon_id=taxon_id,
-        cache=cache,
-    )
-
 
-async def _process_items(
-    context_names: list[str],
-    mode: list[Literal["create", "provide"]],
-    taxon_id: list[str],
-    input_como_dirpath: list[Path] | None,
-    input_matrix_filepath: list[Path] | None,
     output_gene_info_filepath: Path,
     output_trna_config_filepath: Path | None,
     output_mrna_config_filepath: Path | None,

From a348df736ca81c6d566e30df071452b24c6bcc85 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 10:52:46 -0600
Subject: [PATCH 025/345] =?UTF-8?q?=E2=99=BB=EF=B8=8F=20refactor:=20remove?=
 =?UTF-8?q?=20command=20line=20interface?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 main/como/rnaseq_preprocess.py | 122 ---------------------------------
 1 file changed, 122 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index ade8e4d7..47029316 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -684,125 +684,3 @@ async def rnaseq_preprocess(  # noqa: C901
         input_matrix_filepath=input_matrix_filepath,
         cache=cache,
     )
-
-
-def _parse_args():
-    parser = argparse.ArgumentParser(
-        prog="rnaseq_preprocess.py",
-        description="Fetches additional gene information from a provided matrix or gene counts, "
-        "or optionally creates this matrix using gene count files obtained using STAR aligner. "
-        "Creation of counts matrix from STAR aligner output requires that the 'COMO_input' "
-        "folder exists and is correctly structured according to the normalization technique being used. "
-        "A correctly structured folder can be made using our Snakemake-based alignment pipeline at:"
-        "https://github.com/HelikarLab/FastqToGeneCounts",
-        epilog="For additional help, please post questions/issues in the MADRID GitHub repo at"
-        "https://github.com/HelikarLab/COMO",
-    )
-    parser.add_argument(
-        "--context-names",
-        required=True,
-        type=str,
-        nargs="*",
-        help="Tissue/cell name of models to generate. These names should correspond to the folders"
-        "in 'COMO_input/' if creating count matrix files, or to"
-        "'work/data/data_matrices/<context name>/gene_counts_matrix_<context name>.csv' if supplying"
-        "the count matrix as an imported .csv file. If making multiple models in a batch, then"
-        "use the format: 'context1 context2 context3'",
-    )
-    parser.add_argument(
-        "--mode",
-        type=str,
-        nargs="*",
-        required=True,
-        help="Mode of rnaseq_preprocess.py, either 'create' or 'provide'",
-    )
-    parser.add_argument(
-        "--taxon-id",
-        required=False,
-        nargs="*",
-        type=str,
-        default="9606",
-        help="BioDbNet taxon ID number, also accepts 'human', or 'mouse'",
-    )
-    parser.add_argument(
-        "--output-gene-info-filepath",
-        required=False,
-        type=Path,
-        help="The location to write gene information",
-    )
-    parser.add_argument(
-        "--output-count-matrices-dir",
-        required=False,
-        type=str,
-        help="All count matrix files can be placed in a single directory "
-        "if they should not be saved to specific locations",
-    )
-    parser.add_argument(
-        "--output-trna-count-matrix",
-        required=False,
-        type=str,
-        help="The location to save total RNA count matrices",
-    )
-    parser.add_argument(
-        "--output-mrna-count-matrix",
-        required=False,
-        type=str,
-        help="The location to save messenger RNA count matrices",
-    )
-    parser.add_argument(
-        "--output-trna-config-filepath",
-        required=False,
-        default=None,
-        type=Path,
-        help="The location to save TRNA config file",
-    )
-    parser.add_argument(
-        "--output-mrna-config-filepath",
-        required=False,
-        default=None,
-        type=Path,
-        help="The location to save MRNA config file",
-    )
-    parser.add_argument(
-        "--input-como-dirpath",
-        nargs="*",
-        required=False,
-        default=None,
-        type=str,
-        help="Path to COMO input directory",
-    )
-    parser.add_argument(
-        "--input-matrix-filepath",
-        required=False,
-        nargs="*",
-        default=None,
-        type=str,
-        help="Path to input matrix file",
-    )
-    parser.add_argument(
-        "--cache",
-        required=False,
-        type=bool,
-        default=True,
-        help="Cache files for faster processing",
-    )
-    return _Arguments(**vars(parser.parse_args()))
-
-
-if __name__ == "__main__":
-    args: _Arguments = _parse_args()
-    asyncio.run(
-        rnaseq_preprocess(
-            context_names=args.context_names,
-            mode=args.mode,
-            taxon_id=args.taxon_id,
-            output_gene_info_filepath=args.output_gene_info_filepath,
-            output_trna_count_matrix=args.output_trna_count_matrix,
-            output_mrna_count_matrix=args.output_mrna_count_matrix,
-            output_trna_config_filepath=args.output_trna_config_filepath,
-            output_mrna_config_filepath=args.output_mrna_config_filepath,
-            input_como_dirpath=args.input_como_dirpath,
-            input_matrix_filepath=args.input_matrix_filepath,
-            cache=args.cache,
-        )
-    )

From c26e12ecb34b56e18314f9f440f19c636922735b Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 10:53:27 -0600
Subject: [PATCH 026/345] =?UTF-8?q?=E2=99=BB=EF=B8=8F=20refactor:=20define?=
 =?UTF-8?q?=20specific=20input/output=20file=20paths?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 main/como/rnaseq_preprocess.py | 97 +++++++++++++---------------------
 1 file changed, 38 insertions(+), 59 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 47029316..ae226f02 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -615,72 +615,51 @@ async def rnaseq_preprocess(  # noqa: C901
     :param log_level: The logging level
     :param log_location: The logging location
     """
-    context_names = _listify(context_names)
-    mode = _listify(mode)
-    taxon_id = _listify(taxon_id)
-    output_count_matrices_dirpath: list[Path] = [Path(i) for i in _listify(output_count_matrices_dirpath)] if output_count_matrices_dirpath else []  # fmt: skip  # noqa: E501
-    output_trna_count_matrix: list[Path] = [Path(i) for i in _listify(output_trna_count_matrix)] if output_trna_count_matrix else []  # fmt: skip  # noqa: E501
-    output_mrna_count_matrix: list[Path] = [Path(i) for i in _listify(output_mrna_count_matrix)] if output_mrna_count_matrix else []  # fmt: skip  # noqa: E501
-    input_como_dirpath: list[Path] = [Path(i) for i in _listify(input_como_dirpath)] if input_como_dirpath else []
-    input_matrix_filepath: list[Path] = (
-        [Path(i) for i in _listify(input_matrix_filepath)] if input_matrix_filepath else []
-    )
+    with contextlib.suppress(ValueError):
+        logger.remove(0)
+        logger.add(
+            sink=log_location,
+            level=log_level,
+            format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>",  # noqa: E501
+        )
 
-    _validate_matrix_output_args(
-        output_count_matrices_dirpath=output_count_matrices_dirpath,
-        output_trna_count_matrix_filepath=output_trna_count_matrix,
-        output_mrna_count_matrix_filepath=output_mrna_count_matrix,
+    output_gene_info_filepath = output_gene_info_filepath.resolve()
+    como_context_dir = como_context_dir.resolve()
+    input_matrix_filepath = [i.resolve() for i in _listify(input_matrix_filepath)] if input_matrix_filepath else None
+    output_trna_config_filepath = (
+        output_trna_config_filepath.resolve() if output_trna_config_filepath else output_trna_config_filepath
+    )
+    output_polya_config_filepath = (
+        output_polya_config_filepath.resolve() if output_polya_config_filepath else output_polya_config_filepath
+    )
+    output_trna_count_matrix_filepath = (
+        output_trna_count_matrix_filepath.resolve()
+        if output_trna_count_matrix_filepath
+        else output_trna_count_matrix_filepath
+    )
+    output_polya_count_matrix_filepath = (
+        output_polya_count_matrix_filepath.resolve()
+        if output_polya_count_matrix_filepath
+        else output_polya_count_matrix_filepath
     )
 
-    if len(input_como_dirpath) == 0 and len(input_matrix_filepath) == 0:
-        raise ValueError("Either 'como_input_dirpath' or 'input_matrix_filepath' must be provided.")
-
-    if not any({output_trna_config_filepath, output_mrna_config_filepath}):
-        raise ValueError("Either 'output_trna_config_filepath' or 'output_mrna_config_filepath' must be provided.")
-    if output_trna_config_filepath and output_trna_config_filepath.suffix not in {".xlsx", ".xls"}:
-        raise ValueError("output_trna_config_filepath must be an Excel file.")
-    if output_mrna_config_filepath and output_mrna_config_filepath.suffix not in {".xlsx", ".xls"}:
-        raise ValueError("output_mrna_config_filepath must be an Excel file.")
-
-    if not all(m in {"create", "provide"} for m in mode):
-        raise ValueError(f"Invalid mode(s): {', '.join(m for m in mode if m not in {'create', 'provide'})}")
-
-    if not all(t.isdigit() or isinstance(t, int) or t in {"human", "mouse"} for t in taxon_id):
-        raise ValueError("Invalid taxon_id(s). Must be integer, 'human', or 'mouse'.")
+    input_matrix_filepath = _listify(input_matrix_filepath)
+    preparation_method = _listify(preparation_method)
 
-    if not (len(context_names) == len(mode) == len(taxon_id) == len(input_como_dirpath or input_matrix_filepath)):
+    if len(input_matrix_filepath) != len(preparation_method):
         raise ValueError(
-            "context_names, mode, taxon_id, and (como or matrix) input must be the same length.\n"
-            f"context_names: {len(context_names)}\n"
-            f"mode: {len(mode)}\n"
-            f"taxon_id: {len(taxon_id)}\n"
-            f"como_input_dirpath or matrix_filepath: {len(input_como_dirpath or input_matrix_filepath)}"
+            "input_matrix_filepath (--input-matrix-filepath) and "
+            "preparation_method (--preparation-method) must be the same length."
         )
-
-    for path in input_como_dirpath:
-        if not path.exists():
-            raise ValueError(f"COMO input directory does not exist: {path}")
-        if not path.is_dir():
-            raise ValueError(f"COMO input directory must be a directory: {path}")
-
-    for path in input_matrix_filepath:
-        if not path.exists():
-            raise ValueError(f"Input matrix file does not exist: {path}")
-        if path.suffix not in {".csv", ".h5ad"}:
-            raise ValueError(f"Input matrix file must be a .csv or .h5ad file: {path}")
-        if not path.is_file():
-            raise ValueError(f"Input matrix file must be a file: {path}")
-
-    await _process_items(
-        context_names=context_names,
-        mode=mode,
-        taxon_id=taxon_id,
+    await _process(
+        context_name=context_name,
+        taxon=taxon,
+        como_context_dir=como_context_dir,
+        input_matrix_filepath=input_matrix_filepath,
         output_gene_info_filepath=output_gene_info_filepath,
         output_trna_config_filepath=output_trna_config_filepath,
-        output_mrna_config_filepath=output_mrna_config_filepath,
-        output_trna_count_matrix=output_trna_count_matrix,
-        output_mrna_count_matrix=output_mrna_count_matrix,
-        input_como_dirpath=input_como_dirpath,
-        input_matrix_filepath=input_matrix_filepath,
+        output_mrna_config_filepath=output_polya_config_filepath,
+        output_trna_matrix_filepath=output_trna_count_matrix_filepath,
+        output_mrna_matrix_filepath=output_polya_count_matrix_filepath,
         cache=cache,
     )

From 8ee3f0c26ba887656a773906aac49cf2c21327eb Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 10:54:19 -0600
Subject: [PATCH 027/345] =?UTF-8?q?=E2=99=BB=EF=B8=8F=20refactor:=20remove?=
 =?UTF-8?q?=20unnecessary=20code?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 main/como/rnaseq_preprocess.py | 96 +++++++++++++---------------------
 1 file changed, 35 insertions(+), 61 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index ae226f02..3276e90a 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -505,80 +505,54 @@ async def _create_matrix_file(
         subset_config.to_excel(writer, sheet_name=context_name, header=True, index=False)
 
 
+async def _process(
+    context_name: str,
+    taxon: int,
     output_gene_info_filepath: Path,
+    como_context_dir: Path | None,
+    input_matrix_filepath: list[Path] | None,
     output_trna_config_filepath: Path | None,
     output_mrna_config_filepath: Path | None,
-    output_trna_count_matrix: list[Path] | None,
-    output_mrna_count_matrix: list[Path] | None,
+    output_trna_matrix_filepath: Path | None,
+    output_mrna_matrix_filepath: Path | None,
     cache: bool,
 ):
-    tasks = []
-    for i, m in enumerate(mode):
-        if m == "create" and output_trna_config_filepath:
-            tasks.append(
-                asyncio.create_task(
-                    _create_matrix_file(
-                        context_name=context_names[i],
-                        taxon_id=taxon_id[i],
-                        output_gene_matrix_filepath=output_trna_count_matrix[i],
-                        como_dirpath=input_como_dirpath[i],
-                        output_gene_info_filepath=output_gene_info_filepath,
-                        output_config_filepath=output_trna_config_filepath,
-                        rna_type="total",
-                        cache=cache,
-                    )
-                )
-            )
+    rna_types: list[tuple[type_rna, Path, Path]] = []
+    if output_trna_config_filepath:
+        rna_types.append(("total", output_trna_config_filepath, output_trna_matrix_filepath))
+    if output_mrna_config_filepath:
+        rna_types.append(("polya", output_mrna_config_filepath, output_mrna_matrix_filepath))
 
-        if m == "create" and output_mrna_config_filepath:
-            tasks.append(
-                asyncio.create_task(
-                    _create_matrix_file(
-                        context_name=context_names[i],
-                        taxon_id=taxon_id[i],
-                        output_gene_matrix_filepath=output_mrna_count_matrix[i],
-                        como_dirpath=input_como_dirpath[i],
-                        output_gene_info_filepath=output_gene_info_filepath,
-                        output_config_filepath=output_mrna_config_filepath,
-                        rna_type="mrna",
-                        cache=cache,
-                    )
+    # if provided, iterate through como-input specific directories
+    tasks = []
+    for rna, output_config_filepath, output_matrix_filepath in rna_types:
+        tasks.append(
+            asyncio.create_task(
+                _create_matrix_file(
+                    context_name=context_name,
+                    output_config_filepath=output_config_filepath,
+                    como_context_dir=como_context_dir,
+                    output_counts_matrix_filepath=output_matrix_filepath,
+                    rna=rna,
                 )
             )
+        )
 
-        if m == "provide":
-            tasks.append(
-                asyncio.create_task(
-                    _create_gene_info_file(
-                        counts_matrix=input_matrix_filepath[i],
-                        output_filepath=output_gene_info_filepath,
-                        taxon_id=taxon_id[i],
-                        cache=cache,
-                    )
-                )
-            )
     await asyncio.gather(*tasks)
 
+    # create the gene info filepath based on provided data
+    await _create_gene_info_file(
+        counts_matrix_filepaths=[
+            f
+            for f in [*input_matrix_filepath, output_trna_matrix_filepath, output_mrna_matrix_filepath]
+            if f is not None
+        ],
+        output_filepath=output_gene_info_filepath,
+        taxon=taxon,
+        cache=cache,
+    )
 
-def _validate_matrix_output_args(
-    output_count_matrices_dirpath: list,
-    output_trna_count_matrix_filepath: list,
-    output_mrna_count_matrix_filepath: list,
-):
-    def _raise():
-        raise ValueError(
-            "output_count_matrices_dirpath OR "
-            "(output_trna_count_matrix_filepath AND output_mrna_count_matrix_filepath) can be provided"
-        )
 
-    # output_count_matrices_dir OR (output_trna_count_matrix AND output_mrna_count_matrix) can be provided
-    # Check this condition is satisfied
-    if output_count_matrices_dirpath and (output_trna_count_matrix_filepath or output_mrna_count_matrix_filepath):
-        _raise()
-    if output_trna_count_matrix_filepath and not output_mrna_count_matrix_filepath:
-        _raise()
-    if not output_trna_count_matrix_filepath and output_mrna_count_matrix_filepath:
-        _raise()
 
 
 async def rnaseq_preprocess(  # noqa: C901

From 6a9601754febdedb69b28d19095af1aa14ebf7db Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 10:54:45 -0600
Subject: [PATCH 028/345] =?UTF-8?q?=E2=99=BB=EF=B8=8F=20refactor:=20allow?=
 =?UTF-8?q?=20processing=20specific=20filepaths?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 main/como/rnaseq_preprocess.py | 25 ++++++++++++-------------
 1 file changed, 12 insertions(+), 13 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 3276e90a..6d84837b 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -553,21 +553,20 @@ async def _process(
     )
 
 
-
-
-async def rnaseq_preprocess(  # noqa: C901
-    context_names: str | list[str],
-    mode: Literal["create", "provide"] | list[Literal["create", "provide"]],
-    taxon_id: type_taxon | list[type_taxon],
-    input_como_dirpath: type_path | list[type_path] | None = None,
-    input_matrix_filepath: type_path | list[type_path] | None = None,
-    output_gene_info_filepath: Path | None = None,
+async def rnaseq_preprocess(
+    context_name: str,
+    taxon: int,
+    output_gene_info_filepath: Path,
+    como_context_dir: Path | None = None,
+    input_matrix_filepath: Path | list[Path] | None = None,
+    preparation_method: RNAPrepMethod | list[RNAPrepMethod] | None = None,
     output_trna_config_filepath: Path | None = None,
-    output_mrna_config_filepath: Path | None = None,
-    output_count_matrices_dirpath: list[Path] | None = None,
-    output_trna_count_matrix: list[Path] | None = None,
-    output_mrna_count_matrix: list[Path] | None = None,
+    output_polya_config_filepath: Path | None = None,
+    output_trna_count_matrix_filepath: Path | None = None,
+    output_polya_count_matrix_filepath: Path | None = None,
     cache: bool = True,
+    log_level: Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] = "INFO",
+    log_location: str | TextIOWrapper = sys.stderr,
 ) -> None:
     """Preprocesses RNA-seq data for downstream analysis.
 

From c74e83f9716347a91418bac7bc56f21cf4c84073 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 10:58:15 -0600
Subject: [PATCH 029/345] feat: use commitizen for conventional commits

---
 .pre-commit-config.yaml |    7 +-
 pyproject.toml          |    6 +
 uv.lock                 | 1438 +++++++++++++++++++++++++++++++++++++--
 3 files changed, 1393 insertions(+), 58 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 8a1635ef..bf67014b 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,5 +1,6 @@
 repos:
-  - repo: https://github.com/ljnsn/cz-conventional-gitmoji
-    rev: v0.6.1
+  - repo: https://github.com/commitizen-tools/commitizen
+    rev: master
     hooks:
-      - id: conventional-gitmoji
+      - id: commitizen
+        stages: [ commit-msg ]
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 859ddcbe..85a1308f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -62,3 +62,9 @@ troppo = { git = "https://github.com/JoshLoecker/troppo", rev = "update_dependen
 cobamp = { git = "https://github.com/JoshLoecker/cobamp", rev = "update_packages" }
 pydantic-settings = { git = "https://github.com/pydantic/pydantic-settings" }
 
+[tool.commitizen]
+name = "cz_conventional_commits"
+tag_format = "$version"
+version_scheme = "semver2"
+version_provider = "pep621"
+update_changelog_on_bump = true
diff --git a/uv.lock b/uv.lock
index f090d8cc..3a417c7a 100644
--- a/uv.lock
+++ b/uv.lock
@@ -78,6 +78,57 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/3b/00/2344469e2084fb287c2e0b57b72910309874c3245463acd6cf5e3db69324/appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128", size = 9566 },
 ]
 
+[[package]]
+name = "appnope"
+version = "0.1.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321 },
+]
+
+[[package]]
+name = "argcomplete"
+version = "3.5.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/7f/03/581b1c29d88fffaa08abbced2e628c34dd92d32f1adaed7e42fc416938b0/argcomplete-3.5.2.tar.gz", hash = "sha256:23146ed7ac4403b70bd6026402468942ceba34a6732255b9edf5b7354f68a6bb", size = 82341 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a9/37/3fa718aaadd36e073891138dc3ebd919a71bafd4881c97d8a133265af191/argcomplete-3.5.2-py3-none-any.whl", hash = "sha256:036d020d79048a5d525bc63880d7a4b8d1668566b8a76daf1144c0bbe0f63472", size = 43506 },
+]
+
+[[package]]
+name = "argon2-cffi"
+version = "23.1.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "argon2-cffi-bindings" },
+]
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@@ -1704,6 +2969,15 @@ dependencies = [
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From b30a54f5729c0e14c80d6eb1937fa3e2acacb2b4 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 10:58:48 -0600
Subject: [PATCH 030/345] feat: update unit tests for rnaseq_preprocess

---
 tests/fixtures/collect_files.py      | 93 ++++++++++++++++++++++++++++
 tests/unit/test_rnaseq_preprocess.py | 91 +++++++++++++++++++++++++++
 2 files changed, 184 insertions(+)
 create mode 100644 tests/fixtures/collect_files.py
 create mode 100644 tests/unit/test_rnaseq_preprocess.py

diff --git a/tests/fixtures/collect_files.py b/tests/fixtures/collect_files.py
new file mode 100644
index 00000000..6dcefb7e
--- /dev/null
+++ b/tests/fixtures/collect_files.py
@@ -0,0 +1,93 @@
+from pathlib import Path
+from typing import NamedTuple
+
+import pytest
+from _pytest.fixtures import SubRequest
+
+_fragment_size_filepaths = list(Path("main/data/COMO_input").absolute().rglob("*fragment_size*.txt"))
+_gene_count_filepaths = list(Path("main/data/COMO_input").absolute().rglob("*.tab"))
+_insert_size_filepaths = list(Path("main/data/COMO_input").absolute().rglob("*_insert_size.txt"))
+_layout_filepaths = list(Path("main/data/COMO_input").absolute().rglob("*_layout.txt"))
+_preparation_method_filepaths = list(Path("main/data/COMO_input").absolute().rglob("*_prep_method.txt"))
+_strandedness_filepaths = list(Path("main/data/COMO_input").absolute().rglob("*_strandedness.txt"))
+
+
+class PackedFilepaths(NamedTuple):
+    sample_name: str
+    fragment_size: Path
+    gene_count: Path
+    insert_size: Path
+    layout: Path
+    preparation_method: Path
+    strandedness: Path
+
+
+@pytest.fixture(params=_fragment_size_filepaths)
+def fragment_size_filepath(request: SubRequest) -> Path:
+    return request.param
+
+
+@pytest.fixture(params=_gene_count_filepaths)
+def gene_count_filepath(request: SubRequest) -> Path:
+    return request.param
+
+
+@pytest.fixture
+def all_gene_count_filepaths() -> list[Path]:
+    return _gene_count_filepaths
+
+
+@pytest.fixture(params=_insert_size_filepaths)
+def insert_size_filepath(request: SubRequest) -> Path:
+    return request.param
+
+
+@pytest.fixture(params=_layout_filepaths)
+def layout_filepath(request: SubRequest) -> Path:
+    return request.param
+
+
+@pytest.fixture(params=_preparation_method_filepaths)
+def prep_method_filepath(request: SubRequest) -> Path:
+    return request.param
+
+
+@pytest.fixture(params=_strandedness_filepaths)
+def strand_filepath(request: SubRequest) -> Path:
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        file
+        for filepaths in [
+            _fragment_size_filepaths,
+            _gene_count_filepaths,
+            _insert_size_filepaths,
+            _layout_filepaths,
+            _preparation_method_filepaths,
+            _strandedness_filepaths,
+        ]
+        for file in filepaths
+    ]
+)
+def any_como_input_filepath(request: SubRequest) -> Path:
+    return request.param
+
+
+@pytest.fixture(params=[Path("main/data/COMO_input/naiveB").absolute(), Path("main/data/COMO_input/smB").absolute()])
+def como_input_data_directory(request: SubRequest) -> Path:
+    return request.param
+
+
+@pytest.fixture(params=["naiveB", "smB"])
+def packed_filepaths(sample_name: str) -> PackedFilepaths:
+    return PackedFilepaths(
+        sample_name=sample_name,
+        fragment_size=Path(f"main/data/COMO_input/{sample_name}/fragmentSizes/{sample_name}_fragment_size.txt"),
+        gene_count=Path(f"main/data/COMO_input/{sample_name}/geneCounts/{sample_name}.tab"),
+        insert_size=Path(f"main/data/COMO_input/{sample_name}/insertSizes/{sample_name}_insert_size.txt"),
+        layout=Path(f"main/data/COMO_input/{sample_name}/layouts/{sample_name}_layout.txt"),
+        preparation_method=Path(f"main/data/COMO_input/{sample_name}/prepMethods/{sample_name}_prep_method.txt"),
+        strandedness=Path(f"main/data/COMO_input/{sample_name}/strandedness/{sample_name}_strandedness.txt"),
+    )
diff --git a/tests/unit/test_rnaseq_preprocess.py b/tests/unit/test_rnaseq_preprocess.py
new file mode 100644
index 00000000..9483689a
--- /dev/null
+++ b/tests/unit/test_rnaseq_preprocess.py
@@ -0,0 +1,91 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import pandas as pd
+import pytest
+from como.rnaseq_preprocess import (
+    _organize_gene_counts_files,
+    _process_first_multirun_sample,
+    _process_standard_replicate,
+    _sample_name_from_filepath,
+    _STARinformation,
+    _StudyMetrics,
+)
+
+from tests.fixtures.collect_files import (
+    PackedFilepaths,
+    all_gene_count_filepaths,
+    any_como_input_filepath,
+    como_input_data_directory,
+    packed_filepaths,
+    strand_filepath,
+)
+
+
+class TestSTARInformation:
+    valid_data = Path("main/data/COMO_input/naiveB/geneCounts/S1/naiveB_S1R1.tab").absolute()
+    invalid_data = Path("main/data/COMO_input/naiveB/fragmentSizes/S1/naiveB_S1R1_fragment_size.txt").absolute()
+
+    @pytest.mark.asyncio
+    async def test_build_from_tab_valid_file(self):
+        """Validate building STAR information object."""
+        star: _STARinformation = await _STARinformation.build_from_tab(TestSTARInformation.valid_data)
+        assert len(star.gene_names) == len(star.count_matrix) == 61541
+        assert len(star.num_unmapped) == 3
+        assert len(star.num_multimapping) == 3
+        assert len(star.num_no_feature) == 3
+        assert len(star.num_ambiguous) == 3
+
+    @pytest.mark.asyncio
+    async def test_build_from_tab_invalid_file(self):
+        """Validate error on invalid file."""
+        with pytest.raises(ValueError, match="Invalid file format"):
+            await _STARinformation.build_from_tab(TestSTARInformation.invalid_data)
+
+
+def test_sample_name_from_filepath(any_como_input_filepath: Path):  # noqa: F811
+    expected = "_".join(any_como_input_filepath.stem.split("_")[:2])
+    assert _sample_name_from_filepath(any_como_input_filepath) == expected
+
+
+def test_organize_gene_counts_files(como_input_data_directory: Path):  # noqa: F811
+    metrics: list[_StudyMetrics] = _organize_gene_counts_files(como_input_data_directory)
+    for metric in metrics:
+        assert len(metric.sample_names) == metric.num_samples == len(metric.count_files) == len(metric.strand_files)
+
+        for file in metric.count_files:
+            assert f"/{metric.study_name}/" in file.as_posix()
+            assert "geneCounts" in file.as_posix()
+            assert file.suffix == ".tab"
+
+        for file in metric.strand_files:
+            assert f"/{metric.study_name}/" in file.as_posix()
+            assert "strandedness" in file.as_posix()
+            assert file.suffix == ".txt"
+
+
+@pytest.mark.asyncio
+async def test_process_first_multirun_sample(strand_filepath: Path, all_gene_count_filepaths: list[Path]):  # noqa: F811
+    result: pd.DataFrame = await _process_first_multirun_sample(strand_filepath, all_gene_count_filepaths)
+    assert result.columns[0] == "ensembl_gene_id"
+    assert len(result.columns) == 2
+    assert result.columns[1] in strand_filepath.as_posix()
+
+
+def test_pack_filepaths(packed_filepaths: PackedFilepaths):
+    assert packed_filepaths.sample_name in packed_filepaths.fragment_size.as_posix()
+    assert packed_filepaths.sample_name in packed_filepaths.gene_count.as_posix()
+    assert packed_filepaths.sample_name in packed_filepaths.insert_size.as_posix()
+    assert packed_filepaths.sample_name in packed_filepaths.layout.as_posix()
+    assert packed_filepaths.sample_name in packed_filepaths.preparation_method.as_posix()
+    assert packed_filepaths.sample_name in packed_filepaths.strandedness.as_posix()
+
+
+@pytest.mark.asyncio
+async def test_process_standard_replicate(packed_filepaths: PackedFilepaths):
+    await _process_standard_replicate(
+        counts_file=packed_filepaths.gene_count,
+        strand_file=packed_filepaths.strandedness,
+        sample_name=packed_filepaths.sample_name,
+    )

From 2d78d0bb74eddc922825ba9561b3dfc6757e837b Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 11:29:42 -0600
Subject: [PATCH 031/345] feat: allow long lines in jupyter notebook

---
 ruff.toml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/ruff.toml b/ruff.toml
index 455091e3..21802c50 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -48,3 +48,6 @@ ignore = [
     "D103",     # allow undocumented public method definitions
     "S101",     # allow use of `assert` in test files
 ]
+"main/COMO.ipynb" = [
+    "E501",     # allow long lines
+]

From 7768e277bceec615ccfcb31f7453fa73befdfdcc Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 11:29:51 -0600
Subject: [PATCH 032/345] style: ruff formatting

---
 main/COMO.ipynb | 321 +++++++++++++++++++-----------------------------
 1 file changed, 124 insertions(+), 197 deletions(-)

diff --git a/main/COMO.ipynb b/main/COMO.ipynb
index 8178cd67..d361f9b2 100644
--- a/main/COMO.ipynb
+++ b/main/COMO.ipynb
@@ -235,27 +235,66 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T03:30:27.253112Z",
+     "start_time": "2024-12-07T03:30:27.236557Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001B[32m2024-12-06 23:12:10\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m629\u001B[0m - \u001B[1mTEST\u001B[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001B[32m2024-12-06 23:12:11\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m274\u001B[0m - \u001B[32m\u001B[1mWrote gene count matrix for 'total' RNA at '/Users/joshl/Projects/COMO/main/data/results/naiveB/total-rna/totalrna_naiveB.csv'\u001B[0m\n",
+      "\u001B[32m2024-12-06 23:12:11\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m274\u001B[0m - \u001B[32m\u001B[1mWrote gene count matrix for 'polya' RNA at '/Users/joshl/Projects/COMO/main/data/results/naiveB/polya-rna/polyarna_naiveB.csv'\u001B[0m\n",
+      "\u001B[32m2024-12-06 23:12:11\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m451\u001B[0m - \u001B[1mFetching gene info (this may take 1-5 minutes)\u001B[0m\n",
+      "\u001B[32m2024-12-06 23:13:04\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m488\u001B[0m - \u001B[32m\u001B[1mGene Info file written at '/Users/joshl/Projects/COMO/main/data/results/naiveB/gene_info.csv'\u001B[0m\n"
+     ]
+    }
+   ],
    "source": [
-    "context_names = \"naiveB\"\n",
-    "taxon_id = \"human\"  # accepts integer (bioDBnet taxon id) or \"human\" or \"mouse\"\n",
-    "preprocess_mode = \"create\"  # \"create\" or \"provide\"\n",
-    "\n",
-    "# fmt: off\n",
-    "cmd = \" \".join(\n",
-    "    [\n",
-    "        \"python3\", \"como/rnaseq_preprocess.py\",\n",
-    "        \"--context-names\", context_names,\n",
-    "        \"--taxon-id\", taxon_id,\n",
-    "        \"--mode\", preprocess_mode,\n",
-    "    ]\n",
-    ")\n",
-    "# fmt: on\n",
+    "from pathlib import Path\n",
     "\n",
-    "!{cmd}"
+    "from como.rnaseq_preprocess import rnaseq_preprocess\n",
+    "from como.types import RNAPrepMethod\n",
+    "\n",
+    "context_names = [\"naiveB\"]\n",
+    "output_gene_info_filepaths = [Path(f\"data/results/{context}/gene_info.csv\") for context in context_names]\n",
+    "como_context_dirs = [Path(f\"data/COMO_input/{context}\") for context in context_names]\n",
+    "output_trna_filepaths = [Path(f\"data/results/{context}/total-rna/totalrna_{context}.csv\") for context in context_names]\n",
+    "output_polya_filepaths = [Path(f\"data/results/{context}/polya-rna/polyarna_{context}.csv\") for context in context_names]\n",
+    "\n",
+    "\n",
+    "for i in range(len(context_names)):\n",
+    "    await rnaseq_preprocess(\n",
+    "        context_name=context_names[i],\n",
+    "        taxon=9606,\n",
+    "        output_gene_info_filepath=output_gene_info_filepaths[i],\n",
+    "        como_context_dir=como_context_dirs[i],\n",
+    "        output_trna_config_filepath=Path(\"./data/config_sheets/trna_config.xlsx\"),\n",
+    "        output_trna_count_matrix_filepath=output_trna_filepaths[i],\n",
+    "        output_polya_config_filepath=Path(\"./data/config_sheets/mrna_config.xlsx\"),\n",
+    "        output_polya_count_matrix_filepath=output_polya_filepaths[i],\n",
+    "        cache=True,\n",
+    "        log_level=\"INFO\",\n",
+    "    )"
    ]
   },
   {
@@ -329,10 +368,10 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# step 2.2 RNA-seq Analysis for Total RNA-seq library preparation\n",
     "\n",
@@ -385,10 +424,10 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "mrnaseq_config_file = \"mrnaseq_data_inputs_auto.xlsx\"\n",
     "rep_ratio = 0.75\n",
@@ -439,10 +478,10 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "scrnaseq_config_file = \"scrnaseq_data_inputs_auto.xlsx\"\n",
     "rep_ratio = 0.75\n",
@@ -627,10 +666,10 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "expression_requirement = 3\n",
     "requirement_adjust = \"regressive\"\n",
@@ -730,125 +769,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/Users/joshl/Projects/COMO/.venv/lib/python3.10/site-packages/cobamp/wrappers/external_wrappers.py:9: UserWarning: \n",
-      "The wrappers.external_wrappers module will be deprecated in a future release in favour of the wrappers module. \n",
-      "    Available ModelObjectReader classes can still be loaded using cobamp.wrappers.<class>. An appropriate model \n",
-      "    reader can also be created using the get_model_reader function on cobamp.wrappers\n",
-      "  warnings.warn(\n",
-      "\u001B[32m2024-12-04 10:27:00.656\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36mcreate_context_specific_model\u001B[0m:\u001B[36m654\u001B[0m - \u001B[1mCreating 'naiveB' model using 'IMAT' reconstruction and 'GUROBI' solver\u001B[0m\n",
-      "\u001B[32m2024-12-04 10:27:00.656\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mcomo.project\u001B[0m:\u001B[36m__init__\u001B[0m:\u001B[36m30\u001B[0m - \u001B[33m\u001B[1m'data_dir' not provided to Config, using /Users/joshl/Projects/COMO/main/data\u001B[0m\n",
-      "\u001B[32m2024-12-04 10:27:00.656\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mcomo.project\u001B[0m:\u001B[36m__init__\u001B[0m:\u001B[36m36\u001B[0m - \u001B[33m\u001B[1m'config_dir' not provided to Config, using /Users/joshl/Projects/COMO/main/data/config_sheets\u001B[0m\n",
-      "\u001B[32m2024-12-04 10:27:00.656\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36mcomo.project\u001B[0m:\u001B[36m__init__\u001B[0m:\u001B[36m42\u001B[0m - \u001B[33m\u001B[1m'results_dir' not provided to Config, using /Users/joshl/Projects/COMO/main/data/results\u001B[0m\n",
-      "Set parameter WLSAccessID\n",
-      "Set parameter WLSSecret\n",
-      "Set parameter LicenseID to value 898845\n",
-      "Academic license 898845 - for non-commercial use only - registered to jl___@huskers.unl.edu\n",
-      "No defined compartments in model GeneralModelUpdatedV2. Compartments will be deduced heuristically using regular expressions.\n",
-      "Using regular expression found the following compartments:c, e, g, i, l, m, n, r, x\n",
-      "/Users/joshl/Projects/COMO/main/como/utils.py:154: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['1' '10' '100' ... '9992' '9993' '9997']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
-      "  expression_data.loc[:, \"entrez_gene_id\"] = expression_data[\"entrez_gene_id\"].astype(str)\n",
-      "\u001B[32m2024-12-04 10:27:04.957\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36m_map_expression_to_reaction\u001B[0m:\u001B[36m391\u001B[0m - \u001B[1mMapped gene expression to reactions, found 2806 error(s).\u001B[0m\n",
-      "\u001B[32m2024-12-04 10:27:04.959\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36m_build_model\u001B[0m:\u001B[36m468\u001B[0m - \u001B[33m\u001B[1mThe force reaction 'ICDH_m' was not found in the general reference_model. Check BiGG, or the relevant database for your general reference_model, for synonyms.\u001B[0m\n",
-      "\u001B[32m2024-12-04 10:27:04.959\u001B[0m | \u001B[33m\u001B[1mWARNING \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36m_build_model\u001B[0m:\u001B[36m468\u001B[0m - \u001B[33m\u001B[1mThe force reaction 'CATC140_c' was not found in the general reference_model. Check BiGG, or the relevant database for your general reference_model, for synonyms.\u001B[0m\n",
-      "Read LP format model from file /var/folders/6s/9l9z74v90tn8lprrp7fchb9w0000gn/T/tmprdfbwfb2.lp\n",
-      "Reading time = 0.02 seconds\n",
-      ": 5837 rows, 21220 columns, 81018 nonzeros\n",
-      "/Users/joshl/Projects/COMO/.venv/lib/python3.10/site-packages/cobra/core/group.py:147: UserWarning: need to pass in a list\n",
-      "  warn(\"need to pass in a list\")\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_ADCim \"Acetoacetate Decarboxylation (Irreversible), Mitochondrial\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_CHOLtu \"Choline Uniport\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_DOPAtu \"Transport of Dopamine via Uniport\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_HISTAtu \"Histamine Uniport\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_NBAHH_ir \"Nalpha- (Beta-Alanyl)-L-Histidine Hydrolase Ir\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_NRPPHRtu \"Transport of Norepinephrine via Uniport\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_PROSTGE2t2 \"Prostaglandin Uniport\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_PROSTGF2t \"Prostaglandin Uniport\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_SRTNtu \"Serotonin Uniport\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_LINOFATPtc \"Uptake of Linoleic Acid\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_OLEICFATPtc \"Uptake of Octadecenoate\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_PALFATPtc \"Transport of Hexadecanoate\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_RTOTAL2FATPc \"Uptake of Rtotal2\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_RTOTALFATPc \"Uptake of Rtotal\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_THEXDDm \"Hydrolysis of Trans7, 10-Hexadecadienoylcoa to 7Z, 10Z-Hexadecadienoic Acid\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_HEXDTRm \"Hydrolysis of 7, 10, 13-Hexadecatrienoylcoa to 7, 10, 13-Hexadecatrienoic Acid\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_5EIPENCm \"Hydrolysis of 5, 8, 11, 14, 17-Eicosapentenoylcoa to 5, 8, 11, 14, 17-Eicosapentenoic Acid\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_2HATVACIDtep \"efflux of 2-hydroxy-atorvastatin-acid into portal blood\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_2HATVACIDthc \"uptake of 2-hydroxy-atorvastatin-acid into hepatocytes\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_2HATVLACtep \"efflux of 2-hydroxy-atorvastatin-lactone into portal blood\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_2HATVLACthc \"uptake of 2-hydroxy-atorvastatin-lactone into hepatocytes\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_4HATVACIDtep \"efflux of 4-hydroxy-atorvastatin-acid into portal blood\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_4HATVACIDthc \"uptake of 4-hydroxy-atorvastatin-acid into hepatocytes\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_4HATVLACtep \"efflux of 4-hydroxy-atorvastatin-lactone into portal blood\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_4HATVLACthc \"uptake of 4-hydroxy-atorvastatin-lactone into hepatocytes\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_ATVACIDMCTtu \"uptake of atorvastatin by enterocytes by MCT1\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_ATVACYLGLUChc \"glucuronidation of atorvastatin to atorvastatin acyl glucuronide (G2) in hepatocytes\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_ATVLACThc \"uptake of atorvastatin-lactone into hepatocytes\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_ATVLACh2r \"spontaneous conversion of glucuronide (G2) to lactone form of atorvastatin\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_CVM1GLUChc \"glucuronidation of cerivastatin-M1\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_CVM23GLUChc \"glucuronidation of cerivastatin-M23\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_IBUP_Rtdu \"passive diffusion of ibuprofen_R\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_IBUP_Stdu \"passive diffusion of ibuprofen_S\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_PTVSTtu \"uptake of pitavastatin by enterocytes\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_RSVtu \"uptake of rosuvastatin by enterocytes\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_SMVACIDATPteb \"efflux of simvastatin dihydroxy acid form into bile\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_SMVACIDhep \"beta-glucuronidation of simvastatin-acyl-glucuronide to simvastatin dihydroxy acid form\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_SMVGLUCLAChep \"spontaneous conversion of glucuronide to lactone form of simvastatin\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "Error encountered trying to <Setting notes on sbase: <Reaction R_SMVGLUChep \"glucuronidation of the open acid form of simvastatin\">>.\n",
-      "LibSBML error code -3: The requested action could not be performed. This can occur in a variety of contexts, such as passing a null object as a parameter in a situation where it does not make sense to permit a null object.\n",
-      "\u001B[32m2024-12-04 10:27:45.905\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36mcreate_context_specific_model\u001B[0m:\u001B[36m690\u001B[0m - \u001B[32m\u001B[1mSaved output file to /Users/joshl/Projects/COMO/main/data/results/naiveB\u001B[0m\n",
-      "\u001B[32m2024-12-04 10:27:45.905\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36mcreate_context_specific_model\u001B[0m:\u001B[36m691\u001B[0m - \u001B[1mNumber of Genes: 1,477\u001B[0m\n",
-      "\u001B[32m2024-12-04 10:27:45.905\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36mcreate_context_specific_model\u001B[0m:\u001B[36m692\u001B[0m - \u001B[1mNumber of Metabolites: 3,204\u001B[0m\n",
-      "\u001B[32m2024-12-04 10:27:45.905\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36m__main__\u001B[0m:\u001B[36mcreate_context_specific_model\u001B[0m:\u001B[36m693\u001B[0m - \u001B[1mNumber of Reactions: 5,263\u001B[0m\n",
-      "Warning: environment still referenced so free is deferred (Continue to use WLS)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
+    "import json\n",
+    "import os\n",
+    "from pathlib import Path\n",
+    "\n",
+    "from como.project import Config\n",
+    "\n",
     "# Set your objectives before running!\n",
     "objective_dict = {\"naiveB\": \"biomass_maintenance\", \"smB\": \"biomass_maintenance\"}\n",
     "# -----------------\n",
@@ -860,34 +790,28 @@
     "recon_algorithms = [\"IMAT\"]\n",
     "solver = \"GUROBI\"\n",
     "\n",
-    "import json\n",
-    "import os\n",
-    "from pathlib import Path\n",
-    "\n",
-    "from como.project import Config\n",
-    "\n",
     "config = Config()\n",
     "\n",
     "# Load the output of step 1, which is a dictionary that specifies the merged list of active Gene IDs for each tissue\n",
-    "step1_results_file = os.path.join(config.data_dir, \"results\", \"step1_results_files.json\")\n",
-    "with open(step1_results_file) as json_file:\n",
+    "step1_results_file = config.data_dir / \"results\" \"step1_results_files.json\"\n",
+    "with step1_results_file.open(\"r\") as json_file:\n",
     "    context_gene_exp = json.load(json_file)\n",
     "\n",
     "for recon_algorithm in recon_algorithms:\n",
-    "    for context in context_gene_exp.keys():\n",
+    "    for context in context_gene_exp:\n",
     "        objective = objective_dict[context]\n",
     "\n",
     "        if recon_algorithm.upper() in [\"IMAT\", \"TINIT\"]:\n",
-    "            active_genes_filepath = os.path.join(config.data_dir, \"results\", context, f\"model_scores_{context}.csv\")\n",
+    "            active_genes_filepath = config.data_dir / \"results\" / context / f\"model_scores_{context}.csv\"\n",
     "        else:\n",
     "            gene_expression_file = context_gene_exp[context]\n",
     "            active_genes_filename = Path(gene_expression_file).name\n",
-    "            active_genes_filepath = os.path.join(config.data_dir, \"results\", context, active_genes_filename)\n",
+    "            active_genes_filepath = config.data_dir / \"results\" / context / active_genes_filename\n",
     "\n",
-    "        general_model_filepath = os.path.join(config.data_dir, \"GeneralModelUpdatedV2.mat\")\n",
-    "        boundary_reactions_filepath = os.path.join(config.data_dir, \"boundary_rxns\", f\"{context}_boundary_rxns.csv\")\n",
-    "        force_reactions_filepath = os.path.join(config.data_dir, \"force_rxns\", f\"{context}_force_rxns.csv\")\n",
-    "        exclude_reactions_filepath = os.path.join(config.data_dir, \"exclude_rxns\", f\"{context}_exclude_rxns.csv\")\n",
+    "        general_model_filepath = config.data_dir / \"GeneralModelUpdatedV2.mat\"\n",
+    "        boundary_reactions_filepath = config.data_dir / \"boundary_rxns\" / f\"{context}_boundary_rxns.csv\"\n",
+    "        force_reactions_filepath = config.data_dir / \"force_rxns\" / f\"{context}_force_rxns.csv\"\n",
+    "        exclude_reactions_filepath = config.data_dir / \"exclude_rxns\" / f\"{context}_exclude_rxns.csv\"\n",
     "\n",
     "        # fmt: off\n",
     "        cmd = \" \".join(\n",
@@ -941,16 +865,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
-    "from pathlib import Path\n",
-    "\n",
     "import cobra\n",
     "from como.project import Config\n",
     "from escher import Builder\n",
+    "from loguru import logger\n",
     "\n",
     "config = Config()\n",
     "\n",
-    "user_map_dir = Path(f\"{config.data_dir}/local_files/maps/\")\n",
+    "user_map_dir = config.data_dir / \"local_files\" / \"maps\"\n",
     "map_dict = {\n",
     "    \"trypto\": f\"{config.data_dir}/maps/RECON1/RECON1.tryptophan_metabolism.json\",\n",
     "    # \"lipid\": f\"{config.data_dir}/maps/RECON1/RECON1.\",  # Not present in COMO by default yet\n",
@@ -968,20 +890,18 @@
     "    index += 1\n",
     "\n",
     "# Collect any additional maps under the `{config.data_dir}/maps/` directory\n",
-    "for file in Path(f\"{config.data_dir}/maps\").glob(\"**/*.json\"):\n",
+    "for file in (config.data_dir / \"maps\").glob(\"**/*.json\"):\n",
     "    if file not in map_dict.values():\n",
     "        map_dict[file.stem] = file\n",
     "\n",
     "for recon_algorithm in recon_algorithms:\n",
-    "    for context in context_gene_exp.keys():\n",
-    "        # for context in [\"naiveB\", \"smB\"]:\n",
-    "        print(f\"Starting {context}\")\n",
-    "        model_json = os.path.join(config.data_dir, \"results\", context, f\"{context}_SpecificModel_{recon_algorithm}.json\")\n",
+    "    for context in context_gene_exp:\n",
+    "        model_json = config.data_dir / \"results\" / context / f\"{context}_SpecificModel_{recon_algorithm}.json\"\n",
     "\n",
-    "        print(f\"Loading '{context}', this may take some time...\")\n",
+    "        logger.info(f\"Loading '{context}', this may take some time...\")\n",
     "        model = cobra.io.load_json_model(model_json)\n",
-    "        for key in map_dict.keys():\n",
-    "            print(f\"Running with: {key}\")\n",
+    "        for key in map_dict:\n",
+    "            logger.info(f\"Running with: {key}\")\n",
     "            builder = Builder(map_json=str(map_dict[key]))\n",
     "            builder.model = model\n",
     "            solution = cobra.flux_analysis.pfba(model)\n",
@@ -995,19 +915,22 @@
     "            ]\n",
     "            builder.reaction_no_data_color = \"#8e8e8e\"\n",
     "\n",
-    "            builder.save_html(os.path.join(config.data_dir, \"results\", context, \"figures\", f\"{key}_map_{context}_{recon_algorithm}.html\"))\n",
+    "            builder.save_html(\n",
+    "                config.data_dir / \"results\" / context / \"figures\" / f\"{key}_map_{context}_{recon_algorithm}.html\"\n",
+    "            )\n",
     "\n",
-    "        out_dir = os.path.join(config.data_dir, \"results\", context)\n",
-    "        # for algorithm in [\"GIMME\", \"IMAT\", \"FASTCORE\", \"tINIT\"]:\n",
-    "        report_file = os.path.join(out_dir, f\"memote_report_{context}_{recon_algorithm}.html\")\n",
-    "        model_file = os.path.join(out_dir, f\"{context}_SpecificModel_{recon_algorithm}.xml\")\n",
-    "        log_dir = os.path.join(out_dir, \"memote\")\n",
-    "        log_file = os.path.join(log_dir, f\"{context}_{recon_algorithm}_memote.log\")\n",
+    "        out_dir = config.data_dir / \"results\" / context\n",
+    "        report_file = out_dir / f\"memote_report_{context}_{recon_algorithm}.html\"\n",
+    "        model_file = out_dir / f\"{context}_SpecificModel_{recon_algorithm}.xml\"\n",
+    "        log_dir = out_dir / \"memote\"\n",
+    "        log_file = log_dir / f\"{context}_{recon_algorithm}_memote.log\"\n",
     "\n",
-    "        if not os.path.exists(log_dir):\n",
-    "            os.mkdir(log_dir)\n",
+    "        if not log_dir.exists():\n",
+    "            log_dir.mkdir(parents=True, exist_ok=True)\n",
     "\n",
-    "        cmd = \" \".join([\"memote\", \"report\", \"snapshot\", \"--filename\", f\"{report_file}\", f\"{model_file}\", \">\", f\"{log_file}\"])\n",
+    "        cmd = \" \".join(\n",
+    "            [\"memote\", \"report\", \"snapshot\", \"--filename\", f\"{report_file}\", f\"{model_file}\", \">\", f\"{log_file}\"]\n",
+    "        )\n",
     "\n",
     "        !{cmd}"
    ]
@@ -1036,11 +959,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from como.utils import stringlist_to_list\n",
+    "\n",
     "disease_names = [\"arthritis\", \"lupus_a\", \"lupus_b\"]\n",
     "data_source = \"rnaseq\"\n",
     "taxon_id = \"human\"\n",
     "\n",
-    "from como.utils import stringlist_to_list\n",
     "\n",
     "for context_name in stringlist_to_list(context_names):\n",
     "    disease_config_file = f\"disease_data_inputs_{context_name}.xlsx\"\n",
@@ -1100,6 +1024,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import json\n",
+    "\n",
+    "from como.project import Config\n",
+    "from como.utils import stringlist_to_list\n",
+    "from loguru import logger\n",
+    "\n",
     "# Knock out simulation for the analyzed tissues and diseases\n",
     "model_files = {\n",
     "    # \"context_name\": \"/path/to/model.mat\"\n",
@@ -1107,11 +1037,6 @@
     "}\n",
     "sovler = \"gurobi\"\n",
     "\n",
-    "import json\n",
-    "import os\n",
-    "\n",
-    "from como.utils import stringlist_to_list\n",
-    "from como.project import Config\n",
     "\n",
     "config = Config()\n",
     "\n",
@@ -1119,27 +1044,29 @@
     "for context in stringlist_to_list(context_names):\n",
     "    for recon_algorithm in recon_algorithms:\n",
     "        for disease in disease_names:\n",
-    "            disease_path = os.path.join(config.data_dir, \"results\", context, disease)\n",
-    "            out_dir = os.path.join(config.data_dir, \"results\", context, disease)\n",
-    "            tissue_gene_folder = os.path.join(config.data_dir, context)\n",
-    "            os.makedirs(tissue_gene_folder, exist_ok=True)\n",
+    "            disease_path = config.data_dir / \"results\" / context / disease\n",
+    "            out_dir = config.data_dir / \"results\" / context / disease\n",
+    "            tissue_gene_folder = config.data_dir / context\n",
+    "            tissue_gene_folder.mkdir(parents=True, exist_ok=True)\n",
     "\n",
-    "            if not os.path.exists(disease_path):\n",
-    "                print(f\"Disease path doesn't exist! Looking for {disease_path}\")\n",
+    "            if not disease_path.exists():\n",
+    "                logger.warning(f\"Disease path doesn't exist! Looking for {disease_path}\")\n",
     "                continue\n",
     "\n",
     "            # load the results of step 3 to dictionary \"disease_files\"\n",
-    "            step3_results_file = os.path.join(config.data_dir, \"results\", context, disease, \"step2_results_files.json\")\n",
+    "            step3_results_file = config.data_dir / \"results\" / context / disease / \"step2_results_files.json\"\n",
     "\n",
-    "            with open(step3_results_file) as json_file:\n",
+    "            with step3_results_file.open(\"r\") as json_file:\n",
     "                disease_files = json.load(json_file)\n",
     "                down_regulated_disease_genes = disease_files[\"down_regulated\"]\n",
     "                up_regulated_disease_genes = disease_files[\"up_regulated\"]\n",
     "\n",
-    "            if context in model_files.keys():\n",
+    "            if context in model_files:\n",
     "                tissue_specific_model_filepath = model_files[context]\n",
     "            else:\n",
-    "                tissue_specific_model_filepath = os.path.join(config.data_dir, \"results\", context, f\"{context}_SpecificModel_{recon_algorithm}.mat\")\n",
+    "                tissue_specific_model_filepath = (\n",
+    "                    config.data_dir / \"results\" / context / f\"{context}_SpecificModel_{recon_algorithm}.mat\"\n",
+    "                )\n",
     "\n",
     "            # fmt: off\n",
     "            cmd = [\n",
@@ -1156,7 +1083,7 @@
     "            # fmt: on\n",
     "\n",
     "            if recon_algorithm == \"IMAT\":\n",
-    "                cmd.extend([\"--reference-flux-file\", os.path.join(config.data_dir, \"results\", context, \"IMAT_flux.csv\")])\n",
+    "                cmd.extend([\"--reference-flux-file\", config.data_dir / \"results\" / context / \"IMAT_flux.csv\"])\n",
     "\n",
     "            cmd = \" \".join(cmd)\n",
     "            !{cmd}"

From eeef8918b30d6abbbed232d85bed3ef47290bc80 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 11:32:12 -0600
Subject: [PATCH 033/345] fix: ignore F811

---
 ruff.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/ruff.toml b/ruff.toml
index 21802c50..3691008f 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -46,6 +46,7 @@ ignore = [
     "D101",     # allow undocumented public class
     "D102",     # allow undocumented class method
     "D103",     # allow undocumented public method definitions
+    "F811",     # allow redefinition of variables, required for pytest fixtures
     "S101",     # allow use of `assert` in test files
 ]
 "main/COMO.ipynb" = [

From 9b5325ef7ab86238f7a9e99ea2140a2d28b45861 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 11:33:55 -0600
Subject: [PATCH 034/345] fix: duplicate noqa directive

---
 tests/test_proteomics.py             | 2 +-
 tests/unit/test_rnaseq_preprocess.py | 6 +++---
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/tests/test_proteomics.py b/tests/test_proteomics.py
index d3c6ad66..0f38be27 100644
--- a/tests/test_proteomics.py
+++ b/tests/test_proteomics.py
@@ -77,7 +77,7 @@ async def test_ftp_client(self):
         assert await client.quit() is None
 
     @pytest.mark.skip(reason="pyftpdlib is broken, no way to test this")
-    def test_reader(self, ftpserver, fixture_ftp_server, ftp_file_names):  # noqa: F811
+    def test_reader(self, ftpserver, fixture_ftp_server, ftp_file_names):
         # Use pytest_localftpserver and fixtures.fixture_ftp_server.fix
         # Now we can get login information for our local FTP server
         file_extensions: list[str] = ["raw"]
diff --git a/tests/unit/test_rnaseq_preprocess.py b/tests/unit/test_rnaseq_preprocess.py
index 9483689a..8347b790 100644
--- a/tests/unit/test_rnaseq_preprocess.py
+++ b/tests/unit/test_rnaseq_preprocess.py
@@ -44,12 +44,12 @@ async def test_build_from_tab_invalid_file(self):
             await _STARinformation.build_from_tab(TestSTARInformation.invalid_data)
 
 
-def test_sample_name_from_filepath(any_como_input_filepath: Path):  # noqa: F811
+def test_sample_name_from_filepath(any_como_input_filepath: Path):
     expected = "_".join(any_como_input_filepath.stem.split("_")[:2])
     assert _sample_name_from_filepath(any_como_input_filepath) == expected
 
 
-def test_organize_gene_counts_files(como_input_data_directory: Path):  # noqa: F811
+def test_organize_gene_counts_files(como_input_data_directory: Path):
     metrics: list[_StudyMetrics] = _organize_gene_counts_files(como_input_data_directory)
     for metric in metrics:
         assert len(metric.sample_names) == metric.num_samples == len(metric.count_files) == len(metric.strand_files)
@@ -66,7 +66,7 @@ def test_organize_gene_counts_files(como_input_data_directory: Path):  # noqa: F
 
 
 @pytest.mark.asyncio
-async def test_process_first_multirun_sample(strand_filepath: Path, all_gene_count_filepaths: list[Path]):  # noqa: F811
+async def test_process_first_multirun_sample(strand_filepath: Path, all_gene_count_filepaths: list[Path]):
     result: pd.DataFrame = await _process_first_multirun_sample(strand_filepath, all_gene_count_filepaths)
     assert result.columns[0] == "ensembl_gene_id"
     assert len(result.columns) == 2

From 3279563c79cf66002df6a07d3275691afc8e32e1 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 11:34:59 -0600
Subject: [PATCH 035/345] refactor: remove unused testing file

---
 tests/test_rnaseq_preprocess.py | 47 ---------------------------------
 1 file changed, 47 deletions(-)
 delete mode 100644 tests/test_rnaseq_preprocess.py

diff --git a/tests/test_rnaseq_preprocess.py b/tests/test_rnaseq_preprocess.py
deleted file mode 100644
index 752ec333..00000000
--- a/tests/test_rnaseq_preprocess.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from __future__ import annotations
-
-import sys
-
-import pytest
-from como import rnaseq_preprocess
-
-
-# Define a list of arguments to test
-@pytest.mark.skip("Not testing args because the input 'argv' has been removed")
-@pytest.mark.parametrize(
-    "args",
-    [
-        # Test using data in COMO_input data
-        ["--context-names", "naiveB immNK", "--gene-format", "Ensembl", "--taxon-id", "9606", "--create-matrix"],
-        [
-            "--context-names",
-            "dimNK brightNK",
-            "--gene-format",
-            "SYMBOL",
-            "--taxon-id",
-            "human",
-            "--provide-matrix",
-            "--matrix",
-            "COMO_input/counts_matrix.tsv",
-        ],
-    ],
-)
-def test_arg_input(args: list[str]):
-    """Asserts that the arguments passed into the function are correct."""
-    context_names = args[1]
-    gene_format = args[3]
-    taxon_id = args[5]
-    matrix_mode = args[6]
-
-    sys.argv = args
-    parsed = rnaseq_preprocess._parse_args()
-
-    assert [context_name in parsed.context_names for context_name in context_names.split()]
-    assert parsed.gene_format == gene_format
-    assert parsed.taxon_id == taxon_id
-
-    if matrix_mode == "--create-matrix":
-        assert parsed.make_matrix is True
-    elif matrix_mode == "--provide-matrix":
-        assert parsed.make_matrix is False
-        assert parsed.provided_matrix_fname == args[8]

From f262624e7f153631c5f0208938352e447266aaff Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 11:55:30 -0600
Subject: [PATCH 036/345] test: update tests based on changes

---
 tests/fixtures/collect_files.py      | 16 ++++++++--------
 tests/unit/test_rnaseq_preprocess.py | 15 +++------------
 2 files changed, 11 insertions(+), 20 deletions(-)

diff --git a/tests/fixtures/collect_files.py b/tests/fixtures/collect_files.py
index 6dcefb7e..93d59e70 100644
--- a/tests/fixtures/collect_files.py
+++ b/tests/fixtures/collect_files.py
@@ -81,13 +81,13 @@ def como_input_data_directory(request: SubRequest) -> Path:
 
 
 @pytest.fixture(params=["naiveB", "smB"])
-def packed_filepaths(sample_name: str) -> PackedFilepaths:
+def packed_filepaths(request: SubRequest) -> PackedFilepaths:
     return PackedFilepaths(
-        sample_name=sample_name,
-        fragment_size=Path(f"main/data/COMO_input/{sample_name}/fragmentSizes/{sample_name}_fragment_size.txt"),
-        gene_count=Path(f"main/data/COMO_input/{sample_name}/geneCounts/{sample_name}.tab"),
-        insert_size=Path(f"main/data/COMO_input/{sample_name}/insertSizes/{sample_name}_insert_size.txt"),
-        layout=Path(f"main/data/COMO_input/{sample_name}/layouts/{sample_name}_layout.txt"),
-        preparation_method=Path(f"main/data/COMO_input/{sample_name}/prepMethods/{sample_name}_prep_method.txt"),
-        strandedness=Path(f"main/data/COMO_input/{sample_name}/strandedness/{sample_name}_strandedness.txt"),
+        sample_name=request.param,
+        fragment_size=Path(f"main/data/COMO_input/{request.param}/fragmentSizes/{request.param}_fragment_size.txt"),
+        gene_count=Path(f"main/data/COMO_input/{request.param}/geneCounts/{request.param}.tab"),
+        insert_size=Path(f"main/data/COMO_input/{request.param}/insertSizes/{request.param}_insert_size.txt"),
+        layout=Path(f"main/data/COMO_input/{request.param}/layouts/{request.param}_layout.txt"),
+        preparation_method=Path(f"main/data/COMO_input/{request.param}/prepMethods/{request.param}_prep_method.txt"),
+        strandedness=Path(f"main/data/COMO_input/{request.param}/strandedness/{request.param}_strandedness.txt"),
     )
diff --git a/tests/unit/test_rnaseq_preprocess.py b/tests/unit/test_rnaseq_preprocess.py
index 8347b790..c0d49c2e 100644
--- a/tests/unit/test_rnaseq_preprocess.py
+++ b/tests/unit/test_rnaseq_preprocess.py
@@ -24,8 +24,8 @@
 
 
 class TestSTARInformation:
-    valid_data = Path("main/data/COMO_input/naiveB/geneCounts/S1/naiveB_S1R1.tab").absolute()
-    invalid_data = Path("main/data/COMO_input/naiveB/fragmentSizes/S1/naiveB_S1R1_fragment_size.txt").absolute()
+    valid_data = Path("main/data/COMO_input/naiveB/geneCounts/S1/naiveB_S1R1.tab").resolve()
+    invalid_data = Path("main/data/COMO_input/naiveB/fragmentSizes/S1/naiveB_S1R1_fragment_size.txt").resolve()
 
     @pytest.mark.asyncio
     async def test_build_from_tab_valid_file(self):
@@ -40,7 +40,7 @@ async def test_build_from_tab_valid_file(self):
     @pytest.mark.asyncio
     async def test_build_from_tab_invalid_file(self):
         """Validate error on invalid file."""
-        with pytest.raises(ValueError, match="Invalid file format"):
+        with pytest.raises(ValueError, match="Building STAR information requires a '.tab' file"):
             await _STARinformation.build_from_tab(TestSTARInformation.invalid_data)
 
 
@@ -80,12 +80,3 @@ def test_pack_filepaths(packed_filepaths: PackedFilepaths):
     assert packed_filepaths.sample_name in packed_filepaths.layout.as_posix()
     assert packed_filepaths.sample_name in packed_filepaths.preparation_method.as_posix()
     assert packed_filepaths.sample_name in packed_filepaths.strandedness.as_posix()
-
-
-@pytest.mark.asyncio
-async def test_process_standard_replicate(packed_filepaths: PackedFilepaths):
-    await _process_standard_replicate(
-        counts_file=packed_filepaths.gene_count,
-        strand_file=packed_filepaths.strandedness,
-        sample_name=packed_filepaths.sample_name,
-    )

From 02e0a3168fa5db7b7d6e57975d0b57fc7249e28d Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 11:56:17 -0600
Subject: [PATCH 037/345] fix: do not use asyncio.gather

Using asyncio.gather seemed to cause a race condition where the proper number of reads was not always stored in the correct variable
---
 main/como/rnaseq_preprocess.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 6d84837b..ae8bcdea 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -41,9 +41,11 @@ async def build_from_tab(cls, filepath: Path) -> _STARinformation:
             raise ValueError(f"Building STAR information requires a '.tab' file; received: '{filepath}'")
 
         async with aiofiles.open(filepath) as i_stream:
-            unmapped, multimapping, no_feature, ambiguous = await asyncio.gather(
-                *[i_stream.readline(), i_stream.readline(), i_stream.readline(), i_stream.readline()]
-            )
+            unmapped = await i_stream.readline()
+            multimapping = await i_stream.readline()
+            no_feature = await i_stream.readline()
+            ambiguous = await i_stream.readline()
+
             num_unmapped = [int(i) for i in unmapped.rstrip("\n").split("\t")[1:]]
             num_multimapping = [int(i) for i in multimapping.rstrip("\n").split("\t")[1:]]
             num_no_feature = [int(i) for i in no_feature.rstrip("\n").split("\t")[1:]]

From 04b47f32fcd5db9f35deaa2114704ec82e8f41a4 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 12:07:46 -0600
Subject: [PATCH 038/345] fix: use proper type

---
 main/como/rnaseq.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/main/como/rnaseq.py b/main/como/rnaseq.py
index 1c504d97..59ba8a70 100644
--- a/main/como/rnaseq.py
+++ b/main/como/rnaseq.py
@@ -27,9 +27,9 @@
 from scipy.signal import find_peaks
 from sklearn.neighbors import KernelDensity
 
-from como.custom_types import RNASeqPreparationMethod
 from como.migrations import gene_info_migrations
 from como.project import Config
+from como.types import RNAPrepMethod
 from como.utils import convert_gene_data
 
 
@@ -684,7 +684,7 @@ async def save_rnaseq_tests(
     config_filepath: Path,
     gene_info_filepath: Path,
     output_filepath: Path,
-    prep: RNASeqPreparationMethod,
+    prep: RNAPrepMethod,
     taxon_id: Taxon,
     replicate_ratio: float,
     batch_ratio: float,
@@ -702,7 +702,7 @@ async def save_rnaseq_tests(
         high_batch_ratio=high_batch_ratio,
     )
 
-    if prep == RNASeqPreparationMethod.SCRNA:
+    if prep == RNAPrepMethod.SCRNA:
         technique = FilteringTechnique.umi
         logger.warning(
             "Single cell filtration does not normalize and assumes "

From ad0e0074c9554887c48ac396937143b50e1f38de Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 12:08:40 -0600
Subject: [PATCH 039/345] refactor: remove dependency on Config

---
 main/como/rnaseq.py | 11 ++++-------
 1 file changed, 4 insertions(+), 7 deletions(-)

diff --git a/main/como/rnaseq.py b/main/como/rnaseq.py
index 59ba8a70..633aad6c 100644
--- a/main/como/rnaseq.py
+++ b/main/como/rnaseq.py
@@ -28,7 +28,6 @@
 from sklearn.neighbors import KernelDensity
 
 from como.migrations import gene_info_migrations
-from como.project import Config
 from como.types import RNAPrepMethod
 from como.utils import convert_gene_data
 
@@ -528,10 +527,9 @@ def cpm_filter(
     context_name: str,
     metrics: NamedMetrics,
     filtering_options: _FilteringOptions,
-    prep: RNASeqPreparationMethod,
+    output_csv_filepath: Path,
 ) -> NamedMetrics:
     """Apply Counts Per Million (CPM) filtering to the count matrix for a given sample."""
-    config = Config()
     n_exp = filtering_options.replicate_ratio
     n_top = filtering_options.high_replicate_ratio
     cut_off = filtering_options.cut_off
@@ -548,12 +546,11 @@ def cpm_filter(
         #   thus, (0 / 1) * 1_000_000 = 0
         library_size[library_size == 0] = 1
 
-        output_filepath = config.result_dir / context_name / prep.value / f"CPM_Matrix_{prep.value}_{sample}.csv"
-        output_filepath.parent.mkdir(parents=True, exist_ok=True)
+        output_csv_filepath.parent.mkdir(parents=True, exist_ok=True)
         counts_per_million: pd.DataFrame = (counts / library_size) * 1_000_000
         counts_per_million.insert(0, "entrez_gene_ids", pd.Series(entrez_ids))
-        logger.debug(f"Writing CPM matrix to {output_filepath}")
-        counts_per_million.to_csv(output_filepath, index=False)
+        logger.debug(f"Writing CPM matrix to {output_csv_filepath}")
+        counts_per_million.to_csv(output_csv_filepath, index=False)
 
         # TODO: Counts per million is adding ~61,500 columns (equal to the number of genes) for some reason.
         #  Most likely due to multiplying by 1_000_000, not exactly sure why

From c2dce72ce23883981e6a1349c38ca10d5ff1fde5 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 12:08:53 -0600
Subject: [PATCH 040/345] style: ruff formatting

---
 main/como/rnaseq.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/main/como/rnaseq.py b/main/como/rnaseq.py
index 633aad6c..82f09c5b 100644
--- a/main/como/rnaseq.py
+++ b/main/como/rnaseq.py
@@ -524,7 +524,6 @@ def calculate_z_score(metrics: NamedMetrics) -> NamedMetrics:
 
 def cpm_filter(
     *,
-    context_name: str,
     metrics: NamedMetrics,
     filtering_options: _FilteringOptions,
     output_csv_filepath: Path,
@@ -534,9 +533,8 @@ def cpm_filter(
     n_top = filtering_options.high_replicate_ratio
     cut_off = filtering_options.cut_off
 
-    sample: str
     metric: _StudyMetrics
-    for sample, metric in metrics.items():
+    for metric in metrics.values():
         counts: pd.DataFrame = metric.count_matrix
         entrez_ids: list[str] = metric.entrez_gene_ids
         library_size: pd.DataFrame = counts.sum(axis=1)

From 78fcf668efc0fa922305dd4995c1dab564d6565c Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 12:09:09 -0600
Subject: [PATCH 041/345] refactor: require output filepath for CPM

---
 main/como/rnaseq.py | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/main/como/rnaseq.py b/main/como/rnaseq.py
index 82f09c5b..c8ab4e4a 100644
--- a/main/como/rnaseq.py
+++ b/main/como/rnaseq.py
@@ -651,24 +651,31 @@ def zfpkm_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions,
 
 def filter_counts(
     *,
-    context_name: str,
     metrics: NamedMetrics,
     technique: FilteringTechnique,
     filtering_options: _FilteringOptions,
-    prep: RNASeqPreparationMethod,
+    cpm_output_filepath: Path | None = None,
 ) -> NamedMetrics:
     """Filter the count matrix based on the specified technique."""
     match technique:
         case FilteringTechnique.cpm:
+            if cpm_output_filepath is None:
+                raise ValueError("CPM output filepath must be provided")
             return cpm_filter(
-                context_name=context_name, metrics=metrics, filtering_options=filtering_options, prep=prep
+                metrics=metrics,
+                filtering_options=filtering_options,
+                output_csv_filepath=cpm_output_filepath,
             )
+
         case FilteringTechnique.tpm:
             return tpm_quantile_filter(metrics=metrics, filtering_options=filtering_options)
+
         case FilteringTechnique.zfpkm:
             return zfpkm_filter(metrics=metrics, filtering_options=filtering_options, calcualte_fpkm=True)
+
         case FilteringTechnique.umi:
             return zfpkm_filter(metrics=metrics, filtering_options=filtering_options, calcualte_fpkm=False)
+
         case _:
             raise ValueError(f"Technique must be one of {FilteringTechnique}")
 
@@ -716,11 +723,9 @@ async def save_rnaseq_tests(
     entrez_gene_ids = read_counts_results.entrez_gene_ids
 
     metrics = filter_counts(
-        context_name=context_name,
         metrics=metrics,
         technique=technique,
         filtering_options=filtering_options,
-        prep=prep,
     )
 
     expressed_genes: list[str] = []

From f06311f5d30a2335a882a6674d387b54f844ea26 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 12:11:03 -0600
Subject: [PATCH 042/345] style: log message for calcualted genes

---
 main/como/rnaseq.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/main/como/rnaseq.py b/main/como/rnaseq.py
index c8ab4e4a..cf45f39c 100644
--- a/main/como/rnaseq.py
+++ b/main/como/rnaseq.py
@@ -758,6 +758,7 @@ async def save_rnaseq_tests(
 
     boolean_matrix.to_csv(output_filepath, index=False)
     logger.info(
-        f"{context_name} - Found {expressed_count} expressed and {high_confidence_count} confidently expressed genes"
+        f"{context_name} - Found {expressed_count} expressed genes, "
+        f"{high_confidence_count} of which are confidently expressed"
     )
     logger.success(f"Wrote boolean matrix to {output_filepath}")

From 355b9df052fc0c68884687c063ccf0db27b9ad59 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 15:09:03 -0600
Subject: [PATCH 043/345] refactor: remove rnaseq.py

This moves all components of rnaseq.py into rnaseq_gen.py
---
 main/como/rnaseq.py | 764 --------------------------------------------
 1 file changed, 764 deletions(-)
 delete mode 100644 main/como/rnaseq.py

diff --git a/main/como/rnaseq.py b/main/como/rnaseq.py
deleted file mode 100644
index cf45f39c..00000000
--- a/main/como/rnaseq.py
+++ /dev/null
@@ -1,764 +0,0 @@
-from __future__ import annotations
-
-import gc
-import math
-import multiprocessing
-import time
-from collections import namedtuple
-from dataclasses import dataclass, field
-from enum import Enum
-from functools import partial
-from multiprocessing.pool import Pool
-from pathlib import Path
-from typing import Callable, NamedTuple
-
-import numpy as np
-import numpy.typing as npt
-import pandas as pd
-import plotly.graph_objs as go
-import scanpy as sc
-import sklearn
-import sklearn.neighbors
-from fast_bioservices import Taxon
-from fast_bioservices.pipeline import ensembl_to_gene_id_and_symbol
-from loguru import logger
-from pandas import DataFrame
-from plotly.subplots import make_subplots
-from scipy.signal import find_peaks
-from sklearn.neighbors import KernelDensity
-
-from como.migrations import gene_info_migrations
-from como.types import RNAPrepMethod
-from como.utils import convert_gene_data
-
-
-class _FilteringOptions(NamedTuple):
-    replicate_ratio: float
-    batch_ratio: float
-    cut_off: float
-    high_replicate_ratio: float
-    high_batch_ratio: float
-
-
-class FilteringTechnique(Enum):
-    """RNA sequencing filtering capabilities."""
-
-    cpm = "cpm"
-    zfpkm = "zfpkm"
-    tpm = "quantile"
-    umi = "umi"
-
-    @staticmethod
-    def from_string(value: str) -> FilteringTechnique:
-        """Create a filtering technique object from a string."""
-        match value.lower():
-            case "cpm":
-                return FilteringTechnique.cpm
-            case "zfpkm":
-                return FilteringTechnique.zfpkm
-            case "quantile":
-                return FilteringTechnique.tpm
-            case "umi":
-                return FilteringTechnique.umi
-            case _:
-                possible_values = [t.value for t in FilteringTechnique]
-                raise ValueError(f"Filtering technique must be one of {possible_values}; got: {value}")
-
-
-class LayoutMethod(Enum):
-    """RNA sequencing layout method."""
-
-    paired_end = "paired-end"
-    single_end = "single-end"
-
-
-@dataclass
-class _StudyMetrics:
-    study: str
-    num_samples: int
-    count_matrix: pd.DataFrame
-    fragment_lengths: npt.NDArray[np.float32]
-    sample_names: list[str]
-    layout: list[LayoutMethod]
-    entrez_gene_ids: list[str]
-    gene_sizes: npt.NDArray[np.float32]
-    __normalization_matrix: pd.DataFrame = field(default_factory=pd.DataFrame)
-    __z_score_matrix: pd.DataFrame = field(default_factory=pd.DataFrame)
-    __high_confidence_entrez_gene_ids: list[str] = field(default=list)
-
-    def __post_init__(self):
-        for layout in self.layout:
-            if layout not in LayoutMethod:
-                raise ValueError(f"Layout must be 'paired-end' or 'single-end'; got: {layout}")
-
-    @property
-    def normalization_matrix(self) -> pd.DataFrame:
-        return self.__normalization_matrix
-
-    @normalization_matrix.setter
-    def normalization_matrix(self, value: pd.DataFrame) -> None:
-        self.__normalization_matrix = value
-
-    @property
-    def z_score_matrix(self) -> pd.DataFrame:
-        return self.__z_score_matrix
-
-    @z_score_matrix.setter
-    def z_score_matrix(self, value: pd.DataFrame) -> None:
-        self.__z_score_matrix = value
-
-    @property
-    def high_confidence_entrez_gene_ids(self) -> list[str]:
-        return self.__high_confidence_entrez_gene_ids
-
-    @high_confidence_entrez_gene_ids.setter
-    def high_confidence_entrez_gene_ids(self, values: list[str]) -> None:
-        self.__high_confidence_entrez_gene_ids = values
-
-
-Density = namedtuple("Density", ["x", "y"])
-
-
-class _ZFPKMResult(NamedTuple):
-    zfpkm: pd.Series
-    density: Density
-    mu: float
-    std_dev: float
-    max_fpkm: float
-
-
-class _ReadMatrixResults(NamedTuple):
-    metrics: dict[str, _StudyMetrics]
-    entrez_gene_ids: list[str]
-
-
-NamedMetrics = dict[str, _StudyMetrics]
-
-
-def k_over_a(k: int, a: float) -> Callable[[npt.NDArray], bool]:
-    """Return a function that filters rows of an array based on the sum of elements being greater than or equal to A at least k times.
-
-    This code is based on the `kOverA` function found in R's `genefilter` package: https://www.rdocumentation.org/packages/genefilter/versions/1.54.2/topics/kOverA
-
-    :param k: The minimum number of times the sum of elements must be greater than or equal to A.
-    :param a: The value to compare the sum of elements to.
-    :return: A function that accepts a NumPy array to perform the actual filtering
-    """  # noqa: E501
-
-    def filter_func(row: npt.NDArray) -> bool:
-        return np.sum(row >= a) >= k
-
-    return filter_func
-
-
-def genefilter(data: pd.DataFrame | npt.NDArray, filter_func: Callable[[npt.NDArray], bool]) -> npt.NDArray:
-    """Apply a filter function to the rows of the data and return the filtered array.
-
-    This code is based on the `genefilter` function found in R's `genefilter` package: https://www.rdocumentation.org/packages/genefilter/versions/1.54.2/topics/genefilter
-
-    :param data: The data to filter
-    :param filter_func: THe function to filter the data by
-    :return: A NumPy array of the filtered data.
-    """
-    match type(data):
-        case pd.DataFrame:
-            return data.apply(filter_func, axis=1).values
-        case npt.NDArray:
-            return np.apply_along_axis(filter_func, axis=1, arr=data)
-        case _:
-            raise ValueError("Unsupported data type. Must be a Pandas DataFrame or a NumPy array.")
-
-
-async def _read_counts_matrix(
-    *,
-    context_name: str,
-    counts_matrix_filepath: Path,
-    config_filepath: Path,
-    gene_info_filepath: Path,
-    taxon_id: Taxon,
-) -> _ReadMatrixResults:
-    """Read the counts matrix and returns the results.
-
-    :param context_name: The context name being processed. Usually a cell type, but can be any string
-    :param counts_matrix_filepath: The file path to the gene count matrix
-    :param config_filepath: The file path to the Excel configuration file
-    :param gene_info_filepath: The file path to gene information generated by `rnaseq_preprocess.py`
-    :param taxon_id: The NCBI Taxon ID
-    :return: A dataclass `ReadMatrixResults`
-    """
-    logger.trace(f"Reading config_filepath at '{config_filepath}'")
-    config_df: pd.DataFrame = pd.read_excel(config_filepath, sheet_name=context_name, header=0)
-    gene_info: pd.DataFrame = pd.read_csv(gene_info_filepath)
-    gene_info = gene_info[gene_info["ensembl_gene_id"] != "-"].reset_index(drop=True)
-    gene_info = gene_info_migrations(gene_info)
-
-    match counts_matrix_filepath.suffix:
-        case ".csv":
-            logger.debug(f"Reading CSV file at '{counts_matrix_filepath}'")
-            counts_matrix: pd.DataFrame = pd.read_csv(counts_matrix_filepath, header=0)
-            if "ensembl_gene_id" not in counts_matrix.columns:
-                raise ValueError(
-                    f"Counts matrix must contain a column named 'ensembl_gene_id'. "
-                    f"Ensure the file '{counts_matrix_filepath}' contains this column."
-                )
-            conversion = await ensembl_to_gene_id_and_symbol(
-                ids=counts_matrix["ensembl_gene_id"].tolist(), taxon=taxon_id
-            )
-            counts_matrix = counts_matrix.merge(conversion, on="ensembl_gene_id", how="left")
-
-        case ".h5ad":
-            logger.debug(f"Reading h5ad file at '{counts_matrix_filepath}'")
-            adata: sc.AnnData = sc.read_h5ad(counts_matrix_filepath)
-            counts_matrix: pd.DataFrame = adata.to_df().T  # Make sample names the columns and gene data the index
-
-            # Coherce the incoming gene data (i.e., Gene Symbols) into Entrez and Ensembl Gene IDs
-            conversion = await convert_gene_data(counts_matrix.index.tolist(), taxon_id)
-            counts_matrix.index.name = conversion.index.name
-            counts_matrix = counts_matrix.merge(conversion, left_index=True, right_index=True)
-            counts_matrix = counts_matrix[counts_matrix["entrez_gene_id"] != "-"]
-            counts_matrix.reset_index(inplace=True)
-
-            # explicit garbage collection because this function runs for a little while
-            del adata
-            del conversion
-            gc.collect()
-
-        case _:
-            raise ValueError(
-                f"Unknown file extension '{counts_matrix_filepath.suffix}'. Valid options are '.csv' or '.h5ad'."
-            )
-
-    if counts_matrix.empty:
-        raise ValueError(
-            f"Counts matrix is empty. Ensure the file contains data. "
-            f"Attempted to process file '{counts_matrix_filepath}'"
-        )
-
-    # Only include Entrez and Ensembl Gene IDs that are present in `gene_info`
-    counts_matrix["entrez_gene_id"] = counts_matrix["entrez_gene_id"].str.split("//")
-    counts_matrix = counts_matrix.explode("entrez_gene_id")
-    counts_matrix = counts_matrix.replace(to_replace="-", value=pd.NA).dropna()
-    counts_matrix["entrez_gene_id"] = counts_matrix["entrez_gene_id"].astype(int)
-
-    gene_info = gene_info.replace(to_replace="-", value=pd.NA).dropna()
-    gene_info["entrez_gene_id"] = gene_info["entrez_gene_id"].astype(int)
-
-    counts_matrix = counts_matrix.merge(
-        gene_info[["entrez_gene_id", "ensembl_gene_id"]],
-        on=["entrez_gene_id", "ensembl_gene_id"],
-        how="inner",
-    )
-    gene_info = gene_info.merge(
-        counts_matrix[["entrez_gene_id", "ensembl_gene_id"]],
-        on=["entrez_gene_id", "ensembl_gene_id"],
-        how="inner",
-    )
-
-    entrez_gene_ids: list[str] = gene_info["entrez_gene_id"].tolist()
-    metrics: NamedMetrics = {}
-    for study in config_df["study"].unique().tolist():
-        study_sample_names = config_df[config_df["study"] == study]["sample_name"].tolist()
-        layouts = config_df[config_df["study"] == study]["layout"].tolist()
-        metrics[study] = _StudyMetrics(
-            count_matrix=counts_matrix[counts_matrix.columns.intersection(study_sample_names)],
-            fragment_lengths=config_df[config_df["study"] == study]["fragment_length"].values,
-            sample_names=study_sample_names,
-            layout=[LayoutMethod(layout) for layout in layouts],
-            num_samples=len(study_sample_names),
-            entrez_gene_ids=entrez_gene_ids,
-            gene_sizes=np.array(gene_info["size"].values).astype(np.float32),
-            study=study,
-        )
-        metrics[study].fragment_lengths[np.isnan(metrics[study].fragment_lengths)] = 0
-        metrics[study].count_matrix.index = pd.Index(entrez_gene_ids, name="entrez_gene_id")
-
-    return _ReadMatrixResults(metrics=metrics, entrez_gene_ids=gene_info["entrez_gene_id"].tolist())
-
-
-def calculate_tpm(metrics: NamedMetrics) -> NamedMetrics:
-    """Calculate the Transcripts Per Million (TPM) for each sample in the metrics dictionary."""
-    for sample in metrics:
-        count_matrix = metrics[sample].count_matrix
-
-        gene_sizes = metrics[sample].gene_sizes
-
-        tpm_matrix = pd.DataFrame(data=None, index=count_matrix.index, columns=count_matrix.columns)
-        for i in range(len(count_matrix.columns)):
-            values: pd.Series = count_matrix.iloc[:, i] + 1  # Add 1 to prevent division by 0
-            rate = np.log(values.tolist()) - np.log(gene_sizes)
-            denominator = np.log(np.sum(np.exp(rate)))
-            tpm_value = np.exp(rate - denominator + np.log(1e6))
-            tpm_matrix.iloc[:, i] = tpm_value
-        metrics[sample].normalization_matrix = tpm_matrix
-
-    return metrics
-
-
-def calculate_fpkm(metrics: NamedMetrics) -> NamedMetrics:
-    """Calculate the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) for each sample in the metrics dictionary."""  # noqa: E501
-    matrix_values = []
-    for study in metrics:
-        for sample in range(metrics[study].num_samples):
-            layout = metrics[study].layout[sample]
-            count_matrix: npt.NDArray = metrics[study].count_matrix.iloc[:, sample].values
-            gene_size = metrics[study].gene_sizes
-
-            count_matrix = count_matrix.astype(np.float32)
-            gene_size = gene_size.astype(np.float32)
-
-            match layout:
-                case LayoutMethod.paired_end:  # FPKM
-                    mean_fragment_lengths = metrics[study].fragment_lengths[sample]
-                    # Ensure non-negative value
-                    effective_length = [max(0, size - (mean_fragment_lengths + 1)) for size in gene_size]
-                    n = count_matrix.sum()
-                    fpkm = ((count_matrix + 1) * 1e9) / (np.array(effective_length) * n)
-                    matrix_values.append(fpkm)
-                case LayoutMethod.single_end:  # RPKM
-                    # Add a pseudocount before log to ensure log(0) does not happen
-                    rate = np.log(count_matrix + 1) - np.log(gene_size)
-                    exp_rate = np.exp(rate - np.log(np.sum(count_matrix)) + np.log(1e9))
-                    matrix_values.append(exp_rate)
-                case _:
-                    raise ValueError("Invalid normalization method specified")
-
-        fpkm_matrix = pd.DataFrame(matrix_values).T  # Transpose is needed because values were appended as rows
-        fpkm_matrix = fpkm_matrix[~pd.isna(fpkm_matrix)]
-        metrics[study].normalization_matrix = fpkm_matrix
-
-        metrics[study].normalization_matrix.columns = metrics[study].count_matrix.columns
-
-    return metrics
-
-
-def _zfpkm_calculation(col: pd.Series, kernel: KernelDensity, peak_parameters: tuple[float, float]) -> _ZFPKMResult:
-    """Log2 Transformations.
-
-    Stabilize the variance in the data to make the distribution more symmetric; this is helpful for Gaussian fitting
-
-    Kernel Density Estimation (kde)
-        - Non-parametric method to estimate the probability density function (PDF) of a random variable
-        - Estimates the distribution of log2-transformed FPKM values
-        - Bandwidth parameter controls the smoothness of the density estimate
-        - KDE Explanation
-            - A way to smooth a histogram to get a better idea of the underlying distribution of the data
-            - Given a set of data points, we want to understand how they are distributed.
-                Histograms can be useful, but are sensitive to bin size and number
-            - The KDE places a "kernel" - a small symmetric function (i.e., Gaussian curve) - at each data point
-            - The "kernel" acts as a weight, giving more weight to points closer to the center of the kernel,
-                and less weight to points farther away
-            - Kernel functions are summed along each point on the x-axis
-            - A smooth curve is created that represents the estimated density of the data
-
-    Peak Finding
-        - Identifies that are above a certain height and separated by a minimum distance
-        - Represent potential local maxima in the distribution
-
-    Peak Selection
-        - The peak with the highest x-value (from log2-FPKM) is chosen as the mean (mu)
-            of the "inactive" gene distribution
-        - The peak representing unexpressed or inactive genes should be at a lower FPKM
-            value compared to the peak representing expressed genes
-
-    Standard Deviation Estimation
-         - The mean of log2-FPKM values are greater than the calculated mu
-         - Standard deviation is estimated based on the assumption that the right tail of the distribution
-            This represents expressed genes) can be approximated by a half-normal distribution
-
-     zFPKM Transformation
-        - Centers disbribution around 0 and scales it by the standard deviation.
-            This makes it easier to compare gene expression across different samples
-        - Represents the number of standard deviations away from the mean of the "inactive" gene distribution
-        - Higher zFPKM values indicate higher expression levels relative to the "inactive" peak
-        - A zFPKM value of 0 represents the mean of the "inactive" distribution
-        - Research shows that a zFPKM value of -3 or greater can be used as
-            a threshold for calling a gene as "expressed"
-            : https://doi.org/10.1186/1471-2164-14-778
-    """
-    col_log2: npt.NDArray = np.log2(col + 1)
-    col_log2 = np.nan_to_num(col_log2, nan=0)
-    refit: KernelDensity = kernel.fit(col_log2.reshape(-1, 1))  # type: ignore
-
-    # kde: KernelDensity = KernelDensity(kernel="gaussian", bandwidth=bandwidth).fit(col_log2.reshape(-1, 1))
-    x_range = np.linspace(col_log2.min(), col_log2.max(), 1000)
-    density = np.exp(refit.score_samples(x_range.reshape(-1, 1)))
-    peaks, _ = find_peaks(density, height=peak_parameters[0], distance=peak_parameters[1])
-    peak_positions = x_range[peaks]
-
-    mu = 0
-    max_fpkm = 0
-    stddev = 1
-
-    if len(peaks) != 0:
-        mu = peak_positions.max()
-        max_fpkm = density[peaks[np.argmax(peak_positions)]]
-        u = col_log2[col_log2 > mu].mean()
-        stddev = (u - mu) * np.sqrt(np.pi / 2)
-    zfpkm = pd.Series((col_log2 - mu) / stddev, dtype=np.float32, name=col.name)
-
-    return _ZFPKMResult(zfpkm=zfpkm, density=Density(x_range, density), mu=mu, std_dev=stddev, max_fpkm=max_fpkm)
-
-
-def zfpkm_transform(
-    fpkm_df: pd.DataFrame,
-    bandwidth: int = 0.5,
-    peak_parameters: tuple[float, float] = (0.02, 1.0),
-    update_every_percent: float = 0.1,
-) -> tuple[dict[str, _ZFPKMResult], DataFrame]:
-    """Perform zFPKM calculation/transformation."""
-    if update_every_percent > 1:
-        logger.warning(
-            f"update_every_percent should be a decimal value between 0 and 1; got: {update_every_percent} - "
-            f"will convert to percentage"
-        )
-        update_every_percent /= 100
-
-    total = len(fpkm_df.columns)
-    update_per_step: int = int(np.ceil(total * update_every_percent))
-    cores = multiprocessing.cpu_count() - 2
-    logger.debug(f"Processing {total:,} samples through zFPKM transform using {cores} cores")
-    logger.debug(
-        f"Will update every {update_per_step:,} steps as this is approximately "
-        f"{update_every_percent:.1%} of {total:,}"
-    )
-
-    with Pool(processes=cores) as pool:
-        kernel = KernelDensity(kernel="gaussian", bandwidth=bandwidth)
-        chunksize = int(math.ceil(len(fpkm_df.columns) / (4 * cores)))
-        partial_func = partial(_zfpkm_calculation, kernel=kernel, peak_parameters=peak_parameters)
-        chunk_time = time.time()
-        start_time = time.time()
-
-        log_padding = len(str(f"{total:,}"))
-        zfpkm_df = pd.DataFrame(data=0, index=fpkm_df.index, columns=fpkm_df.columns)
-        results: dict[str, _ZFPKMResult] = {}
-        result: _ZFPKMResult
-        for i, result in enumerate(
-            pool.imap(
-                partial_func,
-                (fpkm_df[col] for col in fpkm_df.columns),
-                chunksize=chunksize,
-            )
-        ):
-            key = str(result.zfpkm.name)
-            results[key] = result
-            zfpkm_df[key] = result.zfpkm
-
-            # show updates every X% and at the end, but skip on first iteration
-            if i != 0 and (i % update_per_step == 0 or i == total):
-                current_time = time.time()
-                chunk = current_time - chunk_time
-                total_time = current_time - start_time
-                formatted = f"{i:,}"
-                logger.debug(
-                    f"Processed {formatted:>{log_padding}} of {total:,} - "
-                    f"chunk took {chunk:.1f} seconds - "
-                    f"running for {total_time:.1f} seconds"
-                )
-                chunk_time = current_time
-    return results, zfpkm_df
-
-
-def zfpkm_plot(results, *, plot_xfloor: int = -4, subplot_titles: bool = True):
-    """Plot the log2(FPKM) density and fitted Gaussian for each sample.
-
-    :param results: A dictionary of intermediate results from zfpkm_transform.
-    :param: subplot_titles: Whether to display facet titles (sample names).
-    :param plot_xfloor: Lower limit for the x-axis.
-    :param subplot_titles: Whether to display facet titles (sample names).
-    """
-    mega_df = pd.DataFrame(columns=["sample_name", "log2fpkm", "fpkm_density", "fitted_density_scaled"])
-    for name, result in results.items():
-        stddev = result.std_dev
-        x = np.array(result.density.x)
-        y = np.array(result.density.y)
-
-        fitted = np.exp(-0.5 * ((x - result.mu) / stddev) ** 2) / (stddev * np.sqrt(2 * np.pi))
-        max_fpkm = y.max()
-        max_fitted = fitted.max()
-        scale_fitted = fitted * (max_fpkm / max_fitted)
-
-        df = pd.DataFrame(
-            {
-                "sample_name": [name] * len(x),
-                "log2fpkm": x,
-                "fpkm_density": y,
-                "fitted_density_scaled": scale_fitted,
-            }
-        )
-        mega_df = pd.concat([mega_df, df], ignore_index=True)
-
-    mega_df = mega_df.melt(id_vars=["log2fpkm", "sample_name"], var_name="source", value_name="density")
-    subplot_titles = list(results.keys()) if subplot_titles else None
-    fig = make_subplots(
-        rows=len(results),
-        cols=1,
-        subplot_titles=subplot_titles,
-        vertical_spacing=min(0.05, (1 / (len(results) - 1))),
-    )
-
-    for i, (name, group) in enumerate(mega_df.groupby("sample_name"), start=1):
-        fig.add_trace(
-            trace=go.Scatter(x=group["log2fpkm"], y=group["density"], mode="lines", name=name, legendgroup=name),
-            row=i,
-            col=1,
-        )
-        fig.update_xaxes(title_text="log2(FPKM)", range=[plot_xfloor, max(group["log2fpkm"].tolist())], row=i, col=1)
-        fig.update_yaxes(title_text="density [scaled]", row=i, col=1)
-        fig.update_layout(legend_tracegroupgap=0)
-
-    fig.update_layout(height=600 * len(results), width=1000, title_text="zFPKM Plots", showlegend=True)
-    fig.write_image("zfpkm_plot.png")
-
-
-def calculate_z_score(metrics: NamedMetrics) -> NamedMetrics:
-    """Calculate the z-score for each sample in the metrics dictionary."""
-    for sample in metrics:
-        log_matrix = np.log(metrics[sample].normalization_matrix)
-        z_matrix = pd.DataFrame(
-            data=sklearn.preprocessing.scale(log_matrix, axis=1), columns=metrics[sample].sample_names
-        )
-        metrics[sample].z_score_matrix = z_matrix
-    return metrics
-
-
-def cpm_filter(
-    *,
-    metrics: NamedMetrics,
-    filtering_options: _FilteringOptions,
-    output_csv_filepath: Path,
-) -> NamedMetrics:
-    """Apply Counts Per Million (CPM) filtering to the count matrix for a given sample."""
-    n_exp = filtering_options.replicate_ratio
-    n_top = filtering_options.high_replicate_ratio
-    cut_off = filtering_options.cut_off
-
-    metric: _StudyMetrics
-    for metric in metrics.values():
-        counts: pd.DataFrame = metric.count_matrix
-        entrez_ids: list[str] = metric.entrez_gene_ids
-        library_size: pd.DataFrame = counts.sum(axis=1)
-
-        # For library_sizes equal to 0, add 1 to prevent divide by 0
-        # This will not impact the final counts per million calculation because the original counts are still 0
-        #   thus, (0 / 1) * 1_000_000 = 0
-        library_size[library_size == 0] = 1
-
-        output_csv_filepath.parent.mkdir(parents=True, exist_ok=True)
-        counts_per_million: pd.DataFrame = (counts / library_size) * 1_000_000
-        counts_per_million.insert(0, "entrez_gene_ids", pd.Series(entrez_ids))
-        logger.debug(f"Writing CPM matrix to {output_csv_filepath}")
-        counts_per_million.to_csv(output_csv_filepath, index=False)
-
-        # TODO: Counts per million is adding ~61,500 columns (equal to the number of genes) for some reason.
-        #  Most likely due to multiplying by 1_000_000, not exactly sure why
-
-        min_samples = round(n_exp * len(counts.columns))  # noqa: F841
-        top_samples = round(n_top * len(counts.columns))  # noqa: F841
-        test_bools = pd.DataFrame({"entrez_gene_ids": entrez_ids})
-        for i in range(len(counts_per_million.columns)):
-            cutoff = (
-                10e6 / (np.median(np.sum(counts[:, i])))
-                if cut_off == "default"
-                else 1e6 * cut_off / np.median(np.sum(counts[:, i]))
-            )
-            test_bools = test_bools.merge(counts_per_million[counts_per_million.iloc[:, i] > cutoff])
-
-    return metrics
-
-
-def tpm_quantile_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions) -> NamedMetrics:
-    """Apply quantile-based filtering to the TPM matrix for a given sample."""
-    # TODO: Write the TPM matrix to disk
-
-    n_exp = filtering_options.replicate_ratio
-    n_top = filtering_options.high_replicate_ratio
-    cut_off = filtering_options.cut_off
-    metrics = calculate_tpm(metrics)
-
-    sample: str
-    metric: _StudyMetrics
-    for sample, metric in metrics.items():
-        entrez_ids = metric.entrez_gene_ids
-        gene_size = metric.gene_sizes
-        tpm_matrix: pd.DataFrame = metric.normalization_matrix
-
-        min_samples = round(n_exp * len(tpm_matrix.columns))
-        top_samples = round(n_top * len(tpm_matrix.columns))
-
-        tpm_quantile = tpm_matrix[tpm_matrix > 0]
-        quantile_cutoff = np.quantile(
-            a=tpm_quantile.values, q=1 - (cut_off / 100), axis=0
-        )  # Compute quantile across columns
-        boolean_expression = pd.DataFrame(
-            data=tpm_matrix > quantile_cutoff, index=tpm_matrix.index, columns=tpm_matrix.columns
-        ).astype(int)
-
-        min_func = k_over_a(min_samples, 0.9)
-        top_func = k_over_a(top_samples, 0.9)
-
-        min_genes: npt.NDArray[bool] = genefilter(boolean_expression, min_func)
-        top_genes: npt.NDArray[bool] = genefilter(boolean_expression, top_func)
-
-        # Only keep `entrez_gene_ids` that pass `min_genes`
-        metric.entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, min_genes) if keep]
-        metric.gene_sizes = [gene for gene, keep in zip(gene_size, min_genes) if keep]
-        metric.count_matrix = metric.count_matrix.iloc[min_genes, :]
-        metric.normalization_matrix = metrics[sample].normalization_matrix.iloc[min_genes, :]
-
-        keep_top_genes = [gene for gene, keep in zip(entrez_ids, top_genes) if keep]
-        metric.high_confidence_entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, keep_top_genes) if keep]
-
-    metrics = calculate_z_score(metrics)
-
-    return metrics
-
-
-def zfpkm_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions, calcualte_fpkm: bool) -> NamedMetrics:
-    """Apply zFPKM filtering to the FPKM matrix for a given sample."""
-    min_sample_expression = filtering_options.replicate_ratio
-    high_confidence_sample_expression = filtering_options.high_replicate_ratio
-    cut_off = filtering_options.cut_off
-
-    if calcualte_fpkm:
-        metrics = calculate_fpkm(metrics)
-
-    metric: _StudyMetrics
-    for metric in metrics.values():
-        # if fpkm was not calculated, the normalization matrix will be empty; collect the count matrix instead
-        matrix = metric.count_matrix if metric.normalization_matrix.empty else metric.normalization_matrix
-        matrix = matrix[matrix.sum(axis=1) > 0]
-
-        minimums = matrix == 0
-        results, zfpkm_df = zfpkm_transform(matrix)
-        zfpkm_df[minimums] = -4
-        zfpkm_plot(results)
-
-        # determine which genes are expressed
-        min_samples = round(min_sample_expression * len(zfpkm_df.columns))
-        min_func = k_over_a(min_samples, cut_off)
-        min_genes: npt.NDArray[bool] = genefilter(zfpkm_df, min_func)
-        metric.entrez_gene_ids = [gene for gene, keep in zip(metric.entrez_gene_ids, min_genes) if keep]
-
-        # determine which genes are confidently expressed
-        top_samples = round(high_confidence_sample_expression * len(zfpkm_df.columns))
-        top_func = k_over_a(top_samples, cut_off)
-        top_genes: npt.NDArray[bool] = genefilter(zfpkm_df, top_func)
-        metric.high_confidence_entrez_gene_ids = [gene for gene, keep in zip(metric.entrez_gene_ids, top_genes) if keep]
-
-    return metrics
-
-
-def filter_counts(
-    *,
-    metrics: NamedMetrics,
-    technique: FilteringTechnique,
-    filtering_options: _FilteringOptions,
-    cpm_output_filepath: Path | None = None,
-) -> NamedMetrics:
-    """Filter the count matrix based on the specified technique."""
-    match technique:
-        case FilteringTechnique.cpm:
-            if cpm_output_filepath is None:
-                raise ValueError("CPM output filepath must be provided")
-            return cpm_filter(
-                metrics=metrics,
-                filtering_options=filtering_options,
-                output_csv_filepath=cpm_output_filepath,
-            )
-
-        case FilteringTechnique.tpm:
-            return tpm_quantile_filter(metrics=metrics, filtering_options=filtering_options)
-
-        case FilteringTechnique.zfpkm:
-            return zfpkm_filter(metrics=metrics, filtering_options=filtering_options, calcualte_fpkm=True)
-
-        case FilteringTechnique.umi:
-            return zfpkm_filter(metrics=metrics, filtering_options=filtering_options, calcualte_fpkm=False)
-
-        case _:
-            raise ValueError(f"Technique must be one of {FilteringTechnique}")
-
-
-async def save_rnaseq_tests(
-    context_name: str,
-    counts_matrix_filepath: Path,
-    config_filepath: Path,
-    gene_info_filepath: Path,
-    output_filepath: Path,
-    prep: RNAPrepMethod,
-    taxon_id: Taxon,
-    replicate_ratio: float,
-    batch_ratio: float,
-    high_replicate_ratio: float,
-    high_batch_ratio: float,
-    technique: FilteringTechnique,
-    cut_off: int | float,
-):
-    """Save the results of the RNA-Seq tests to a CSV file."""
-    filtering_options = _FilteringOptions(
-        replicate_ratio=replicate_ratio,
-        batch_ratio=batch_ratio,
-        cut_off=cut_off,
-        high_replicate_ratio=high_replicate_ratio,
-        high_batch_ratio=high_batch_ratio,
-    )
-
-    if prep == RNAPrepMethod.SCRNA:
-        technique = FilteringTechnique.umi
-        logger.warning(
-            "Single cell filtration does not normalize and assumes "
-            "gene counts are counted with Unique Molecular Identifiers (UMIs). "
-            "Setting filtering technique to UMI now."
-        )
-
-    read_counts_results: _ReadMatrixResults = await _read_counts_matrix(
-        context_name=context_name,
-        counts_matrix_filepath=counts_matrix_filepath,
-        config_filepath=config_filepath,
-        gene_info_filepath=gene_info_filepath,
-        taxon_id=taxon_id,
-    )
-    metrics = read_counts_results.metrics
-    entrez_gene_ids = read_counts_results.entrez_gene_ids
-
-    metrics = filter_counts(
-        metrics=metrics,
-        technique=technique,
-        filtering_options=filtering_options,
-    )
-
-    expressed_genes: list[str] = []
-    top_genes: list[str] = []
-    for metric in metrics.values():
-        expressed_genes.extend(metric.entrez_gene_ids)
-        top_genes.extend(metric.high_confidence_entrez_gene_ids)
-
-    expression_frequency = pd.Series(expressed_genes).value_counts()
-    expression_df = pd.DataFrame(
-        {"entrez_gene_id": expression_frequency.index, "frequency": expression_frequency.values}
-    )
-    expression_df["prop"] = expression_df["frequency"] / len(metrics)
-    expression_df = expression_df[expression_df["prop"] >= filtering_options.batch_ratio]
-
-    top_frequency = pd.Series(top_genes).value_counts()
-    top_df = pd.DataFrame({"entrez_gene_id": top_frequency.index, "frequency": top_frequency.values})
-    top_df["prop"] = top_df["frequency"] / len(metrics)
-    top_df = top_df[top_df["prop"] >= filtering_options.high_batch_ratio]
-
-    boolean_matrix = pd.DataFrame(data={"entrez_gene_id": entrez_gene_ids, "expressed": 0, "high": 0})
-    for gene in entrez_gene_ids:
-        if gene in expression_df["entrez_gene_id"]:
-            boolean_matrix.loc[gene, "expressed"] = 1
-        if gene in top_df["entrez_gene_id"]:
-            boolean_matrix.loc[gene, "high"] = 1
-
-    expressed_count = len(boolean_matrix[boolean_matrix["expressed"] == 1])
-    high_confidence_count = len(boolean_matrix[boolean_matrix["high"] == 1])
-
-    boolean_matrix.to_csv(output_filepath, index=False)
-    logger.info(
-        f"{context_name} - Found {expressed_count} expressed genes, "
-        f"{high_confidence_count} of which are confidently expressed"
-    )
-    logger.success(f"Wrote boolean matrix to {output_filepath}")

From a43b20dd4b4771b941bfc1c24c087ed1860cc94e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 15:09:23 -0600
Subject: [PATCH 044/345] refactor: import items from rnaseq.py

---
 main/como/rnaseq_gen.py | 31 ++++++++++++++++++++++++-------
 1 file changed, 24 insertions(+), 7 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 47aeb8ee..134ada3a 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -1,16 +1,33 @@
 from __future__ import annotations
 
-import argparse
-import asyncio
-from dataclasses import dataclass
+import math
+import multiprocessing
+import time
+from collections import namedtuple
+from dataclasses import dataclass, field
+from enum import Enum
+from functools import partial
+from multiprocessing.pool import Pool
+from pathlib import Path
+from typing import Callable, NamedTuple
 
+import numpy as np
+import numpy.typing as npt
 import pandas as pd
-from fast_bioservices import Taxon
+import plotly.graph_objs as go
+import scanpy as sc
+import sklearn
+import sklearn.neighbors
+from fast_bioservices.pipeline import ensembl_to_gene_id_and_symbol
 from loguru import logger
+from pandas import DataFrame
+from plotly.subplots import make_subplots
+from scipy.signal import find_peaks
+from sklearn.neighbors import KernelDensity
 
-from como import Config
-from como.custom_types import RNAPrepMethod
-from como.rnaseq import FilteringTechnique, save_rnaseq_tests
+from como.migrations import gene_info_migrations
+from como.project import Config
+from como.types import RNAPrepMethod
 
 
 @dataclass

From 5675a357a49b99589bd415c5b25a3e951cf55f5f Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 15:10:02 -0600
Subject: [PATCH 045/345] refactor: remove command line usage

---
 main/como/rnaseq_gen.py | 134 +---------------------------------------
 1 file changed, 3 insertions(+), 131 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 134ada3a..db3803a1 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -30,17 +30,13 @@
 from como.types import RNAPrepMethod
 
 
-@dataclass
-class _Arguments:
-    config_file: str
+class _FilteringOptions(NamedTuple):
     replicate_ratio: float
     batch_ratio: float
+    cut_off: float
     high_replicate_ratio: float
     high_batch_ratio: float
-    filtering_technique: FilteringTechnique
-    minimum_cutoff: int | str
-    library_prep: RNAPrepMethod
-    taxon: Taxon
+
 
     def __post_init__(self):
         self.library_prep = RNAPrepMethod.from_string(str(self.library_prep))
@@ -200,128 +196,4 @@ async def rnaseq_gen(
     )
 
 
-def _parse_args() -> _Arguments:
-    parser = argparse.ArgumentParser(
-        prog="rnaseq_gen.py",
-        description="Generate a list of active and high-confidence genes from a counts matrix using a user defined "
-        "at normalization-technique at /work/data/results/<context name>/rnaseq_<context_name>.csv: "
-        "https://github.com/HelikarLab/FastqToGeneCounts",
-        epilog="For additional help, please post questions/issues in the MADRID GitHub repo at "
-        "https://github.com/HelikarLab/MADRID or email babessell@gmail.com",
-    )
-    parser.add_argument(
-        "-c",
-        "--config-file",
-        type=str,
-        required=True,
-        dest="config_file",
-        help="Name of config .xlsx file in the /work/data/config_files/. Can be generated using "
-        "rnaseq_preprocess.py or manually created and imported into the Juypterlab",
-    )
-    parser.add_argument(
-        "-r",
-        "--replicate-ratio",
-        type=float,
-        required=False,
-        default=0.5,
-        dest="replicate_ratio",
-        help="Ratio of replicates required for a gene to be active within that study/batch group "
-        "Example: 0.7 means that for a gene to be active, at least 70% of replicates in a group "
-        "must pass the cutoff after normalization",
-    )
-    parser.add_argument(
-        "-g",
-        "--batch-ratio",
-        type=float,
-        required=False,
-        default=0.5,
-        dest="batch_ratio",
-        help="Ratio of groups (studies or batches) required for a gene to be active "
-        "Example: 0.7 means that for a gene to be active, at least 70% of groups in a study must  "
-        "have passed the replicate ratio test",
-    )
-    parser.add_argument(
-        "-rh",
-        "--high-replicate-ratio",
-        type=float,
-        required=False,
-        default=1.0,
-        dest="high_replicate_ratio",
-        help="Ratio of replicates required for a gene to be considered high-confidence. "
-        "High-confidence genes ignore consensus with other data-sources, such as proteomics. "
-        "Example: 0.9 means that for a gene to be high-confidence, "
-        "at least 90% of replicates in a group must pass the cutoff after normalization",
-    )
-    parser.add_argument(
-        "-gh",
-        "--high-batch-ratio",
-        type=float,
-        required=False,
-        default=1.0,
-        dest="high_batch_ratio",
-        help="Ratio of studies/batches required for a gene to be considered high-confidence within that group. "
-        "High-confidence genes ignore consensus with other data-sources, like proteomics. "
-        "Example: 0.9 means that for a gene to be high-confidence, "
-        "at least 90% of groups in a study must have passed the replicate ratio test",
-    )
-    parser.add_argument(
-        "--taxon",
-        "--taxon-id",
-        type=str,
-        required=True,
-        dest="taxon",
-        help="The NCBI Taxonomy ID that is being proessed. '9606' for humans, '10090' for mice.",
-    )
-    parser.add_argument(
-        "-t",
-        "--filt-technique",
-        type=str,
-        required=False,
-        default="quantile",
-        dest="filtering_technique",
-        help="Technique to normalize and filter counts with. "
-        "Either 'zfpkm', 'quantile', or 'cpm'. More info about each method is discussed in pipeline.ipynb.",
-    )
-    parser.add_argument(
-        "--minimum-cutoff",
-        type=int,
-        required=False,
-        default=None,
-        dest="minimum_cutoff",
-        help="The minimum cutoff used for the filtration technique. "
-        "If the filtering technique is zFPKM, the default is -3. "
-        "If the filtering technique is quantile-tpm, the default is 25. "
-        "If the filtering technique is flat-cpm, the default is determined dynamically. "
-        "If the filtering technique is quantile, the default is 25.",
-    )
-    parser.add_argument(
-        "-p",
-        "--library-prep",
-        required=True,
-        choices=["total", "mrna", "scrna"],
-        dest="library_prep",
-        help="Library preparation method. "
-        "Will separate samples into groups to only compare similarly prepared libraries. "
-        "For example, mRNA, total-rna, scRNA, etc",
-    )
-    args = parser.parse_args()
-    args.filtering_technique = args.filtering_technique.lower()
-    args.taxon = Taxon.from_int(int(args.taxon)) if str(args.taxon).isdigit() else Taxon.from_string(str(args.taxon))  # type: ignore
-    return _Arguments(**vars(args))
-
-
-if __name__ == "__main__":
-    args = _parse_args()
-    asyncio.run(
-        rnaseq_gen(
-            config_filename=args.config_file,
-            replicate_ratio=args.replicate_ratio,
-            batch_ratio=args.batch_ratio,
-            high_replicate_ratio=args.high_replicate_ratio,
-            high_batch_ratio=args.high_batch_ratio,
-            technique=args.filtering_technique,
-            cut_off=args.minimum_cutoff,
-            prep=args.library_prep,
-            taxon_id=args.taxon,
-        )
     )

From b5a199c73bd4f3513960d9c15aecfde8b4d62c2f Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 15:10:23 -0600
Subject: [PATCH 046/345] refactor: add classes for rna processing

---
 main/como/rnaseq_gen.py | 120 ++++++++++++++++++++++++++++------------
 1 file changed, 86 insertions(+), 34 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index db3803a1..6966980a 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -38,45 +38,97 @@ class _FilteringOptions(NamedTuple):
     high_batch_ratio: float
 
 
+class FilteringTechnique(Enum):
+    """RNA sequencing filtering capabilities."""
+
+    cpm = "cpm"
+    zfpkm = "zfpkm"
+    tpm = "quantile"
+    umi = "umi"
+
+    @staticmethod
+    def from_string(value: str) -> FilteringTechnique:
+        """Create a filtering technique object from a string."""
+        match value.lower():
+            case "cpm":
+                return FilteringTechnique.cpm
+            case "zfpkm":
+                return FilteringTechnique.zfpkm
+            case "quantile":
+                return FilteringTechnique.tpm
+            case "umi":
+                return FilteringTechnique.umi
+            case _:
+                possible_values = [t.value for t in FilteringTechnique]
+                raise ValueError(f"Got a filtering technique of '{value}'; should be one of: {possible_values}")
+
+
+class LayoutMethod(Enum):
+    """RNA sequencing layout method."""
+
+    paired_end = "paired-end"
+    single_end = "single-end"
+
+
+@dataclass
+class _StudyMetrics:
+    study: str
+    num_samples: int
+    count_matrix: pd.DataFrame
+    fragment_lengths: npt.NDArray[np.float32]
+    sample_names: list[str]
+    layout: list[LayoutMethod]
+    entrez_gene_ids: list[str]
+    gene_sizes: npt.NDArray[np.float32]
+    __normalization_matrix: pd.DataFrame = field(default_factory=pd.DataFrame)
+    __z_score_matrix: pd.DataFrame = field(default_factory=pd.DataFrame)
+    __high_confidence_entrez_gene_ids: list[str] = field(default=list)
+
     def __post_init__(self):
-        self.library_prep = RNAPrepMethod.from_string(str(self.library_prep))
-        self.filtering_technique = FilteringTechnique.from_string(str(self.filtering_technique))
+        for layout in self.layout:
+            if layout not in LayoutMethod:
+                raise ValueError(f"Layout must be 'paired-end' or 'single-end'; got: {layout}")
 
-        if self.minimum_cutoff is None:
-            if self.filtering_technique == FilteringTechnique.tpm:
-                self.minimum_cutoff = 25
-            elif self.filtering_technique == FilteringTechnique.cpm:
-                self.minimum_cutoff = "default"
-            elif self.filtering_technique == FilteringTechnique.zfpkm:
-                self.minimum_cutoff = -3
+    @property
+    def normalization_matrix(self) -> pd.DataFrame:
+        return self.__normalization_matrix
 
+    @normalization_matrix.setter
+    def normalization_matrix(self, value: pd.DataFrame) -> None:
+        self.__normalization_matrix = value
 
-async def _handle_context_batch(
-    config_filename: str,
-    replicate_ratio: float,
-    batch_ratio: float,
-    replicate_ratio_high: float,
-    batch_ratio_high: float,
-    technique: FilteringTechnique,
-    cut_off: int | float | str,
-    prep: RNAPrepMethod,
-    taxon: Taxon,
-) -> None:
-    """Iterate through each context type and create rnaseq expression file.
+    @property
+    def z_score_matrix(self) -> pd.DataFrame:
+        return self.__z_score_matrix
 
-    :param config_filename: The configuration filename to read
-    :param replicate_ratio: The percentage of replicates that a gene must
-        appear in for a gene to be marked as "active" in a batch/study
-    :param batch_ratio: The percentage of batches that a gene must appear in for a gene to be marked as 'active"
-    :param replicate_ratio_high: The percentage of replicates that a gene must
-        appear in for a gene to be marked "highly confident" in its expression in a batch/study
-    :param batch_ratio_high: The percentage of batches that a gene must
-        appear in for a gene to be marked "highly confident" in its expression
-    :param technique: The filtering technique to use
-    :param cut_off: The cutoff value to use for the provided filtering technique
-    :param prep: The library preparation method
-    :param taxon: The NCBI Taxon ID
-    :return: None
+    @z_score_matrix.setter
+    def z_score_matrix(self, value: pd.DataFrame) -> None:
+        self.__z_score_matrix = value
+
+    @property
+    def high_confidence_entrez_gene_ids(self) -> list[str]:
+        return self.__high_confidence_entrez_gene_ids
+
+    @high_confidence_entrez_gene_ids.setter
+    def high_confidence_entrez_gene_ids(self, values: list[str]) -> None:
+        self.__high_confidence_entrez_gene_ids = values
+
+
+class _ZFPKMResult(NamedTuple):
+    zfpkm: pd.Series
+    density: Density
+    mu: float
+    std_dev: float
+    max_fpkm: float
+
+
+class _ReadMatrixResults(NamedTuple):
+    metrics: dict[str, _StudyMetrics]
+    entrez_gene_ids: list[str]
+
+
+Density = namedtuple("Density", ["x", "y"])
+NamedMetrics = dict[str, _StudyMetrics]
     """
     config = Config()
 

From 5a2dd33e8f24f373ad8db2a417678f10d74973c3 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 15:11:10 -0600
Subject: [PATCH 047/345] feat: added k_over_a calculation

---
 main/como/rnaseq_gen.py | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 6966980a..c9ef8562 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -129,6 +129,22 @@ class _ReadMatrixResults(NamedTuple):
 
 Density = namedtuple("Density", ["x", "y"])
 NamedMetrics = dict[str, _StudyMetrics]
+
+
+def k_over_a(k: int, a: float) -> Callable[[npt.NDArray], bool]:
+    """Return a function that filters rows of an array based on the sum of elements being greater than or equal to A at least k times.
+
+    This code is based on the `kOverA` function found in R's `genefilter` package: https://www.rdocumentation.org/packages/genefilter/versions/1.54.2/topics/kOverA
+
+    :param k: The minimum number of times the sum of elements must be greater than or equal to A.
+    :param a: The value to compare the sum of elements to.
+    :return: A function that accepts a NumPy array to perform the actual filtering
+    """  # noqa: E501
+
+    def filter_func(row: npt.NDArray) -> bool:
+        return np.sum(row >= a) >= k
+
+    return filter_func
     """
     config = Config()
 

From fbb2b765be8f90e548cc56ef82a884baaf85673e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 15:11:24 -0600
Subject: [PATCH 048/345] feat: added genefilter function

---
 main/como/rnaseq_gen.py | 25 +++++++++++++++++++------
 1 file changed, 19 insertions(+), 6 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index c9ef8562..2ad3390b 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -145,14 +145,27 @@ def filter_func(row: npt.NDArray) -> bool:
         return np.sum(row >= a) >= k
 
     return filter_func
+
+
+def genefilter(data: pd.DataFrame | npt.NDArray, filter_func: Callable[[npt.NDArray], bool]) -> npt.NDArray:
+    """Apply a filter function to the rows of the data and return the filtered array.
+
+    This code is based on the `genefilter` function found in R's `genefilter` package: https://www.rdocumentation.org/packages/genefilter/versions/1.54.2/topics/genefilter
+
+    :param data: The data to filter
+    :param filter_func: THe function to filter the data by
+    :return: A NumPy array of the filtered data.
     """
-    config = Config()
+    if not isinstance(data, (pd.DataFrame, npt.NDArray)):
+        raise TypeError("Unsupported data type. Must be a Pandas DataFrame or a NumPy array.")
+
+    return (
+        data.apply(filter_func, axis=1).values
+        if isinstance(data, pd.DataFrame)
+        else np.apply_along_axis(filter_func, axis=1, arr=data)
+    )
+
 
-    config_filepath = config.config_dir / config_filename
-    if not config_filepath.exists():
-        raise FileNotFoundError(f"Unable to find '{config_filename}' at the path: '{config_filepath}'")
-    xl = pd.ExcelFile(config_filepath)
-    sheet_names = xl.sheet_names
 
     logger.info(f"Reading config file: {config_filepath}")
 

From 1de1717baad4bb3d029b0bd243a0523b887caf5e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 15:12:08 -0600
Subject: [PATCH 049/345] refactor: create separate read_counts matrix

Reduce complexity of building matrix results
---
 main/como/rnaseq_gen.py | 16 +++++++++++++++-
 1 file changed, 15 insertions(+), 1 deletion(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 2ad3390b..795e21d3 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -166,8 +166,22 @@ def genefilter(data: pd.DataFrame | npt.NDArray, filter_func: Callable[[npt.NDAr
     )
 
 
+async def _read_counts(path: Path) -> pd.DataFrame:
+    if path.suffix not in {".csv", ".h5ad"}:
+        raise ValueError(f"Unknown file extension '{path.suffix}'. Valid options are '.csv' or '.h5ad'.")
+
+    matrix: pd.DataFrame
+    if path.suffix == ".csv":
+        logger.debug(f"Reading CSV file at '{path}'")
+        matrix = pd.read_csv(path, header=0)
+    elif path.suffix == ".h5ad":
+        logger.debug(f"Reading h5ad file at '{path}'")
+        # Make sample names the columns and gene data the index
+        matrix = sc.read_h5ad(path).to_df().T
+
+    return matrix
+
 
-    logger.info(f"Reading config file: {config_filepath}")
 
     for context_name in sheet_names:
         logger.debug(f"Starting '{context_name}'")

From 279f7cc513e9bfb2501f432ede90f4f42a470024 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 15:12:44 -0600
Subject: [PATCH 050/345] feat: bring zfpkm_filter from rnaseq.py

---
 main/como/rnaseq_gen.py | 35 +++++++++++++++++++++++++++++++++++
 1 file changed, 35 insertions(+)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 795e21d3..600bfa23 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -222,6 +222,41 @@ async def _read_counts(path: Path) -> pd.DataFrame:
         logger.success(f"Results saved at '{rnaseq_output_filepath}'")
 
 
+def zfpkm_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions, calcualte_fpkm: bool) -> NamedMetrics:
+    """Apply zFPKM filtering to the FPKM matrix for a given sample."""
+    min_sample_expression = filtering_options.replicate_ratio
+    high_confidence_sample_expression = filtering_options.high_replicate_ratio
+    cut_off = filtering_options.cut_off
+
+    if calcualte_fpkm:
+        metrics = calculate_fpkm(metrics)
+
+    metric: _StudyMetrics
+    for metric in metrics.values():
+        # if fpkm was not calculated, the normalization matrix will be empty; collect the count matrix instead
+        matrix = metric.count_matrix if metric.normalization_matrix.empty else metric.normalization_matrix
+        matrix = matrix[matrix.sum(axis=1) > 0]
+
+        minimums = matrix == 0
+        results, zfpkm_df = zfpkm_transform(matrix)
+        zfpkm_df[minimums] = -4
+        zfpkm_plot(results)
+
+        # determine which genes are expressed
+        min_samples = round(min_sample_expression * len(zfpkm_df.columns))
+        min_func = k_over_a(min_samples, cut_off)
+        min_genes: npt.NDArray[bool] = genefilter(zfpkm_df, min_func)
+        metric.entrez_gene_ids = [gene for gene, keep in zip(metric.entrez_gene_ids, min_genes) if keep]
+
+        # determine which genes are confidently expressed
+        top_samples = round(high_confidence_sample_expression * len(zfpkm_df.columns))
+        top_func = k_over_a(top_samples, cut_off)
+        top_genes: npt.NDArray[bool] = genefilter(zfpkm_df, top_func)
+        metric.high_confidence_entrez_gene_ids = [gene for gene, keep in zip(metric.entrez_gene_ids, top_genes) if keep]
+
+    return metrics
+
+
 async def rnaseq_gen(
     # config_filepath: Path,
     config_filename: str,

From 7e2fe3d663a91b928a42e5979d33c5f67b71f479 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 15:13:28 -0600
Subject: [PATCH 051/345] feat: added matrix builder

This is functionally equivalent to the _read_counts_matrix function from rnaseq.py
---
 main/como/rnaseq_gen.py | 60 +++++++++++++++++++++++++++++++++++++++--
 1 file changed, 58 insertions(+), 2 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 600bfa23..572f3d5c 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -182,9 +182,65 @@ async def _read_counts(path: Path) -> pd.DataFrame:
     return matrix
 
 
+async def _build_matrix_results(
+    *,
+    matrix: pd.DataFrame,
+    gene_info: pd.DataFrame,
+    metadata_df: pd.DataFrame,
+    taxon: int,
+) -> _ReadMatrixResults:
+    """Read the counts matrix and returns the results.
+
+    :param matrix: The gene counts matrix to process
+    :param metadata_df: The configuration dataframe related to the current context
+    :param taxon: The NCBI Taxon ID
+    :return: A dataclass `ReadMatrixResults`
+    """
+    gene_info = gene_info_migrations(gene_info)
+    conversion = await ensembl_to_gene_id_and_symbol(ids=matrix["ensembl_gene_id"].tolist(), taxon=taxon)
+    matrix = matrix.merge(conversion, on="ensembl_gene_id", how="left")
+
+    # Only include Entrez and Ensembl Gene IDs that are present in `gene_info`
+    matrix["entrez_gene_id"] = matrix["entrez_gene_id"].str.split("//")
+    matrix = matrix.explode("entrez_gene_id")
+    matrix = matrix.replace(to_replace="-", value=pd.NA).dropna()
+    matrix["entrez_gene_id"] = matrix["entrez_gene_id"].astype(int)
+
+    gene_info = gene_info.replace(to_replace="-", value=pd.NA).dropna()
+    gene_info["entrez_gene_id"] = gene_info["entrez_gene_id"].astype(int)
+
+    counts_matrix = matrix.merge(
+        gene_info[["entrez_gene_id", "ensembl_gene_id"]],
+        on=["entrez_gene_id", "ensembl_gene_id"],
+        how="inner",
+    )
+    gene_info = gene_info.merge(
+        counts_matrix[["entrez_gene_id", "ensembl_gene_id"]],
+        on=["entrez_gene_id", "ensembl_gene_id"],
+        how="inner",
+    )
+
+    entrez_gene_ids: list[str] = gene_info["entrez_gene_id"].tolist()
+    metrics: NamedMetrics = {}
+    for study in metadata_df["study"].unique().tolist():
+        study_sample_names = metadata_df[metadata_df["study"] == study]["sample_name"].tolist()
+        layouts = metadata_df[metadata_df["study"] == study]["layout"].tolist()
+        metrics[study] = _StudyMetrics(
+            count_matrix=counts_matrix[counts_matrix.columns.intersection(study_sample_names)],
+            fragment_lengths=metadata_df[metadata_df["study"] == study]["fragment_length"].values,
+            sample_names=study_sample_names,
+            layout=[LayoutMethod(layout) for layout in layouts],
+            num_samples=len(study_sample_names),
+            entrez_gene_ids=entrez_gene_ids,
+            gene_sizes=np.array(gene_info["size"].values).astype(np.float32),
+            study=study,
+        )
+        metrics[study].fragment_lengths[np.isnan(metrics[study].fragment_lengths)] = 0
+        metrics[study].count_matrix.index = pd.Index(entrez_gene_ids, name="entrez_gene_id")
+
+    return _ReadMatrixResults(metrics=metrics, entrez_gene_ids=gene_info["entrez_gene_id"].tolist())
+
 
-    for context_name in sheet_names:
-        logger.debug(f"Starting '{context_name}'")
 
         rnaseq_input_filepath = (
             config.data_dir / "data_matrices" / context_name / f"gene_counts_matrix_{prep.value}_{context_name}"

From 9eaa2acc1b669024abb4ec57a74bc35f25ee912e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 15:13:58 -0600
Subject: [PATCH 052/345] feat: added tpm calculation

---
 main/como/rnaseq_gen.py | 19 +++++++++++++++++--
 1 file changed, 17 insertions(+), 2 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 572f3d5c..9238eda2 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -241,9 +241,24 @@ async def _build_matrix_results(
     return _ReadMatrixResults(metrics=metrics, entrez_gene_ids=gene_info["entrez_gene_id"].tolist())
 
 
+def calculate_tpm(metrics: NamedMetrics) -> NamedMetrics:
+    """Calculate the Transcripts Per Million (TPM) for each sample in the metrics dictionary."""
+    for sample in metrics:
+        count_matrix = metrics[sample].count_matrix
+
+        gene_sizes = metrics[sample].gene_sizes
+
+        tpm_matrix = pd.DataFrame(data=None, index=count_matrix.index, columns=count_matrix.columns)
+        for i in range(len(count_matrix.columns)):
+            values: pd.Series = count_matrix.iloc[:, i] + 1  # Add 1 to prevent division by 0
+            rate = np.log(values.tolist()) - np.log(gene_sizes)
+            denominator = np.log(np.sum(np.exp(rate)))
+            tpm_value = np.exp(rate - denominator + np.log(1e6))
+            tpm_matrix.iloc[:, i] = tpm_value
+        metrics[sample].normalization_matrix = tpm_matrix
+
+    return metrics
 
-        rnaseq_input_filepath = (
-            config.data_dir / "data_matrices" / context_name / f"gene_counts_matrix_{prep.value}_{context_name}"
         )
         if prep == RNAPrepMethod.SCRNA:
             rnaseq_input_filepath = rnaseq_input_filepath.with_suffix(".h5ad")

From 3670016c55cc53e7060288b42242b25fe3911185 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 15:14:11 -0600
Subject: [PATCH 053/345] feat: added fpkm calculation

---
 main/como/rnaseq_gen.py | 37 +++++++++++++++++++++++++++++++++++++
 1 file changed, 37 insertions(+)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 9238eda2..e9288bb3 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -259,6 +259,43 @@ def calculate_tpm(metrics: NamedMetrics) -> NamedMetrics:
 
     return metrics
 
+
+def calculate_fpkm(metrics: NamedMetrics) -> NamedMetrics:
+    """Calculate the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) for each sample in the metrics dictionary."""  # noqa: E501
+    matrix_values = []
+    for study in metrics:
+        for sample in range(metrics[study].num_samples):
+            layout = metrics[study].layout[sample]
+            count_matrix: npt.NDArray = metrics[study].count_matrix.iloc[:, sample].values
+            gene_size = metrics[study].gene_sizes
+
+            count_matrix = count_matrix.astype(np.float32)
+            gene_size = gene_size.astype(np.float32)
+
+            match layout:
+                case LayoutMethod.paired_end:  # FPKM
+                    mean_fragment_lengths = metrics[study].fragment_lengths[sample]
+                    # Ensure non-negative value
+                    effective_length = [max(0, size - (mean_fragment_lengths + 1)) for size in gene_size]
+                    n = count_matrix.sum()
+                    fpkm = ((count_matrix + 1) * 1e9) / (np.array(effective_length) * n)
+                    matrix_values.append(fpkm)
+                case LayoutMethod.single_end:  # RPKM
+                    # Add a pseudocount before log to ensure log(0) does not happen
+                    rate = np.log(count_matrix + 1) - np.log(gene_size)
+                    exp_rate = np.exp(rate - np.log(np.sum(count_matrix)) + np.log(1e9))
+                    matrix_values.append(exp_rate)
+                case _:
+                    raise ValueError("Invalid normalization method specified")
+
+        fpkm_matrix = pd.DataFrame(matrix_values).T  # Transpose is needed because values were appended as rows
+        fpkm_matrix = fpkm_matrix[~pd.isna(fpkm_matrix)]
+        metrics[study].normalization_matrix = fpkm_matrix
+
+        metrics[study].normalization_matrix.columns = metrics[study].count_matrix.columns
+
+    return metrics
+
         )
         if prep == RNAPrepMethod.SCRNA:
             rnaseq_input_filepath = rnaseq_input_filepath.with_suffix(".h5ad")

From 1cd4dbb00a523a6a8bf50c5f166714d8d28681bb Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 15:14:33 -0600
Subject: [PATCH 054/345] feat: added zfpkm transformation and calculation

---
 main/como/rnaseq_gen.py | 139 ++++++++++++++++++++++++++++++++++++----
 1 file changed, 127 insertions(+), 12 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index e9288bb3..7b88962e 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -296,19 +296,134 @@ def calculate_fpkm(metrics: NamedMetrics) -> NamedMetrics:
 
     return metrics
 
+
+def _zfpkm_calculation(col: pd.Series, kernel: KernelDensity, peak_parameters: tuple[float, float]) -> _ZFPKMResult:
+    """Log2 Transformations.
+
+    Stabilize the variance in the data to make the distribution more symmetric; this is helpful for Gaussian fitting
+
+    Kernel Density Estimation (kde)
+        - Non-parametric method to estimate the probability density function (PDF) of a random variable
+        - Estimates the distribution of log2-transformed FPKM values
+        - Bandwidth parameter controls the smoothness of the density estimate
+        - KDE Explanation
+            - A way to smooth a histogram to get a better idea of the underlying distribution of the data
+            - Given a set of data points, we want to understand how they are distributed.
+                Histograms can be useful, but are sensitive to bin size and number
+            - The KDE places a "kernel" - a small symmetric function (i.e., Gaussian curve) - at each data point
+            - The "kernel" acts as a weight, giving more weight to points closer to the center of the kernel,
+                and less weight to points farther away
+            - Kernel functions are summed along each point on the x-axis
+            - A smooth curve is created that represents the estimated density of the data
+
+    Peak Finding
+        - Identifies that are above a certain height and separated by a minimum distance
+        - Represent potential local maxima in the distribution
+
+    Peak Selection
+        - The peak with the highest x-value (from log2-FPKM) is chosen as the mean (mu)
+            of the "inactive" gene distribution
+        - The peak representing unexpressed or inactive genes should be at a lower FPKM
+            value compared to the peak representing expressed genes
+
+    Standard Deviation Estimation
+         - The mean of log2-FPKM values are greater than the calculated mu
+         - Standard deviation is estimated based on the assumption that the right tail of the distribution
+            This represents expressed genes) can be approximated by a half-normal distribution
+
+     zFPKM Transformation
+        - Centers disbribution around 0 and scales it by the standard deviation.
+            This makes it easier to compare gene expression across different samples
+        - Represents the number of standard deviations away from the mean of the "inactive" gene distribution
+        - Higher zFPKM values indicate higher expression levels relative to the "inactive" peak
+        - A zFPKM value of 0 represents the mean of the "inactive" distribution
+        - Research shows that a zFPKM value of -3 or greater can be used as
+            a threshold for calling a gene as "expressed"
+            : https://doi.org/10.1186/1471-2164-14-778
+    """
+    col_log2: npt.NDArray = np.log2(col + 1)
+    col_log2 = np.nan_to_num(col_log2, nan=0)
+    refit: KernelDensity = kernel.fit(col_log2.reshape(-1, 1))  # type: ignore
+
+    # kde: KernelDensity = KernelDensity(kernel="gaussian", bandwidth=bandwidth).fit(col_log2.reshape(-1, 1))
+    x_range = np.linspace(col_log2.min(), col_log2.max(), 1000)
+    density = np.exp(refit.score_samples(x_range.reshape(-1, 1)))
+    peaks, _ = find_peaks(density, height=peak_parameters[0], distance=peak_parameters[1])
+    peak_positions = x_range[peaks]
+
+    mu = 0
+    max_fpkm = 0
+    stddev = 1
+
+    if len(peaks) != 0:
+        mu = peak_positions.max()
+        max_fpkm = density[peaks[np.argmax(peak_positions)]]
+        u = col_log2[col_log2 > mu].mean()
+        stddev = (u - mu) * np.sqrt(np.pi / 2)
+    zfpkm = pd.Series((col_log2 - mu) / stddev, dtype=np.float32, name=col.name)
+
+    return _ZFPKMResult(zfpkm=zfpkm, density=Density(x_range, density), mu=mu, std_dev=stddev, max_fpkm=max_fpkm)
+
+
+def zfpkm_transform(
+    fpkm_df: pd.DataFrame,
+    bandwidth: int = 0.5,
+    peak_parameters: tuple[float, float] = (0.02, 1.0),
+    update_every_percent: float = 0.1,
+) -> tuple[dict[str, _ZFPKMResult], DataFrame]:
+    """Perform zFPKM calculation/transformation."""
+    if update_every_percent > 1:
+        logger.warning(
+            f"update_every_percent should be a decimal value between 0 and 1; got: {update_every_percent} - "
+            f"will convert to percentage"
         )
-        if prep == RNAPrepMethod.SCRNA:
-            rnaseq_input_filepath = rnaseq_input_filepath.with_suffix(".h5ad")
-        elif prep in {RNAPrepMethod.TOTAL, RNAPrepMethod.MRNA}:
-            rnaseq_input_filepath = rnaseq_input_filepath.with_suffix(".csv")
-
-        if not rnaseq_input_filepath.exists():
-            logger.warning(f"Gene counts matrix not found at {rnaseq_input_filepath}, skipping...")
-            continue
-
-        gene_info_filepath = config.data_dir / "gene_info.csv"
-        rnaseq_output_filepath = (
-            config.result_dir / context_name / prep.value / f"rnaseq_{prep.value}_{context_name}.csv"
+        update_every_percent /= 100
+
+    total = len(fpkm_df.columns)
+    update_per_step: int = int(np.ceil(total * update_every_percent))
+    cores = multiprocessing.cpu_count() - 2
+    logger.debug(f"Processing {total:,} samples through zFPKM transform using {cores} cores")
+    logger.debug(
+        f"Will update every {update_per_step:,} steps as this is approximately "
+        f"{update_every_percent:.1%} of {total:,}"
+    )
+
+    with Pool(processes=cores) as pool:
+        kernel = KernelDensity(kernel="gaussian", bandwidth=bandwidth)
+        chunksize = int(math.ceil(len(fpkm_df.columns) / (4 * cores)))
+        partial_func = partial(_zfpkm_calculation, kernel=kernel, peak_parameters=peak_parameters)
+        chunk_time = time.time()
+        start_time = time.time()
+
+        log_padding = len(str(f"{total:,}"))
+        zfpkm_df = pd.DataFrame(data=0, index=fpkm_df.index, columns=fpkm_df.columns)
+        results: dict[str, _ZFPKMResult] = {}
+        result: _ZFPKMResult
+        for i, result in enumerate(
+            pool.imap(
+                partial_func,
+                (fpkm_df[col] for col in fpkm_df.columns),
+                chunksize=chunksize,
+            )
+        ):
+            key = str(result.zfpkm.name)
+            results[key] = result
+            zfpkm_df[key] = result.zfpkm
+
+            # show updates every X% and at the end, but skip on first iteration
+            if i != 0 and (i % update_per_step == 0 or i == total):
+                current_time = time.time()
+                chunk = current_time - chunk_time
+                total_time = current_time - start_time
+                formatted = f"{i:,}"
+                logger.debug(
+                    f"Processed {formatted:>{log_padding}} of {total:,} - "
+                    f"chunk took {chunk:.1f} seconds - "
+                    f"running for {total_time:.1f} seconds"
+                )
+                chunk_time = current_time
+    return results, zfpkm_df
+
         )
         rnaseq_output_filepath.parent.mkdir(parents=True, exist_ok=True)
 

From dd8698e3a9b7ca1311b1d0beda96150d8d45ea77 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 15:14:50 -0600
Subject: [PATCH 055/345] feat: added zfpkm plotting

---
 main/como/rnaseq_gen.py | 67 ++++++++++++++++++++++++++++++-----------
 1 file changed, 50 insertions(+), 17 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 7b88962e..7c35ea3b 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -424,25 +424,58 @@ def zfpkm_transform(
                 chunk_time = current_time
     return results, zfpkm_df
 
+
+def zfpkm_plot(results, *, plot_xfloor: int = -4, subplot_titles: bool = True):
+    """Plot the log2(FPKM) density and fitted Gaussian for each sample.
+
+    :param results: A dictionary of intermediate results from zfpkm_transform.
+    :param: subplot_titles: Whether to display facet titles (sample names).
+    :param plot_xfloor: Lower limit for the x-axis.
+    :param subplot_titles: Whether to display facet titles (sample names).
+    """
+    mega_df = pd.DataFrame(columns=["sample_name", "log2fpkm", "fpkm_density", "fitted_density_scaled"])
+    for name, result in results.items():
+        stddev = result.std_dev
+        x = np.array(result.density.x)
+        y = np.array(result.density.y)
+
+        fitted = np.exp(-0.5 * ((x - result.mu) / stddev) ** 2) / (stddev * np.sqrt(2 * np.pi))
+        max_fpkm = y.max()
+        max_fitted = fitted.max()
+        scale_fitted = fitted * (max_fpkm / max_fitted)
+
+        df = pd.DataFrame(
+            {
+                "sample_name": [name] * len(x),
+                "log2fpkm": x,
+                "fpkm_density": y,
+                "fitted_density_scaled": scale_fitted,
+            }
         )
-        rnaseq_output_filepath.parent.mkdir(parents=True, exist_ok=True)
-
-        await save_rnaseq_tests(
-            context_name=context_name,
-            counts_matrix_filepath=rnaseq_input_filepath,
-            config_filepath=config_filepath,
-            output_filepath=rnaseq_output_filepath.as_posix(),
-            gene_info_filepath=gene_info_filepath,
-            prep=prep,
-            replicate_ratio=replicate_ratio,
-            batch_ratio=batch_ratio,
-            high_replicate_ratio=replicate_ratio_high,
-            high_batch_ratio=batch_ratio_high,
-            technique=technique,
-            cut_off=cut_off,
-            taxon_id=taxon,
+        mega_df = pd.concat([mega_df, df], ignore_index=True)
+
+    mega_df = mega_df.melt(id_vars=["log2fpkm", "sample_name"], var_name="source", value_name="density")
+    subplot_titles = list(results.keys()) if subplot_titles else None
+    fig = make_subplots(
+        rows=len(results),
+        cols=1,
+        subplot_titles=subplot_titles,
+        vertical_spacing=min(0.05, (1 / (len(results) - 1))),
+    )
+
+    for i, (name, group) in enumerate(mega_df.groupby("sample_name"), start=1):
+        fig.add_trace(
+            trace=go.Scatter(x=group["log2fpkm"], y=group["density"], mode="lines", name=name, legendgroup=name),
+            row=i,
+            col=1,
         )
-        logger.success(f"Results saved at '{rnaseq_output_filepath}'")
+        fig.update_xaxes(title_text="log2(FPKM)", range=[plot_xfloor, max(group["log2fpkm"].tolist())], row=i, col=1)
+        fig.update_yaxes(title_text="density [scaled]", row=i, col=1)
+        fig.update_layout(legend_tracegroupgap=0)
+
+    fig.update_layout(height=600 * len(results), width=1000, title_text="zFPKM Plots", showlegend=True)
+    fig.write_image("zfpkm_plot.png")
+
 
 
 def zfpkm_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions, calcualte_fpkm: bool) -> NamedMetrics:

From 01db1efc7fbe76bb49579b7b68a52b2ab6b7c36e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 15:15:03 -0600
Subject: [PATCH 056/345] feat: aded calculate z score

---
 main/como/rnaseq_gen.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 7c35ea3b..6b57dd55 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -477,6 +477,16 @@ def zfpkm_plot(results, *, plot_xfloor: int = -4, subplot_titles: bool = True):
     fig.write_image("zfpkm_plot.png")
 
 
+def calculate_z_score(metrics: NamedMetrics) -> NamedMetrics:
+    """Calculate the z-score for each sample in the metrics dictionary."""
+    for sample in metrics:
+        log_matrix = np.log(metrics[sample].normalization_matrix)
+        z_matrix = pd.DataFrame(
+            data=sklearn.preprocessing.scale(log_matrix, axis=1), columns=metrics[sample].sample_names
+        )
+        metrics[sample].z_score_matrix = z_matrix
+    return metrics
+
 
 def zfpkm_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions, calcualte_fpkm: bool) -> NamedMetrics:
     """Apply zFPKM filtering to the FPKM matrix for a given sample."""

From a47f752059e68241588dd3bbcc6a34a119729615 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 15:15:15 -0600
Subject: [PATCH 057/345] feat: added cpm filtering

---
 main/como/rnaseq_gen.py | 48 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 48 insertions(+)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 6b57dd55..4d04dcef 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -488,6 +488,54 @@ def calculate_z_score(metrics: NamedMetrics) -> NamedMetrics:
     return metrics
 
 
+def cpm_filter(
+    *,
+    context_name: str,
+    metrics: NamedMetrics,
+    filtering_options: _FilteringOptions,
+    prep: RNAPrepMethod,
+) -> NamedMetrics:
+    """Apply Counts Per Million (CPM) filtering to the count matrix for a given sample."""
+    config = Config()
+    n_exp = filtering_options.replicate_ratio
+    n_top = filtering_options.high_replicate_ratio
+    cut_off = filtering_options.cut_off
+
+    sample: str
+    metric: _StudyMetrics
+    for sample, metric in metrics.items():
+        counts: pd.DataFrame = metric.count_matrix
+        entrez_ids: list[str] = metric.entrez_gene_ids
+        library_size: pd.DataFrame = counts.sum(axis=1)
+
+        # For library_sizes equal to 0, add 1 to prevent divide by 0
+        # This will not impact the final counts per million calculation because the original counts are still 0
+        #   thus, (0 / 1) * 1_000_000 = 0
+        library_size[library_size == 0] = 1
+
+        output_filepath = config.result_dir / context_name / prep.value / f"CPM_Matrix_{prep.value}_{sample}.csv"
+        output_filepath.parent.mkdir(parents=True, exist_ok=True)
+        counts_per_million: pd.DataFrame = (counts / library_size) * 1_000_000
+        counts_per_million.insert(0, "entrez_gene_ids", pd.Series(entrez_ids))
+        logger.debug(f"Writing CPM matrix to {output_filepath}")
+        counts_per_million.to_csv(output_filepath, index=False)
+
+        # TODO: Counts per million is adding ~61,500 columns (equal to the number of genes) for some reason.
+        #  Most likely due to multiplying by 1_000_000, not exactly sure why
+
+        min_samples = round(n_exp * len(counts.columns))  # noqa: F841
+        top_samples = round(n_top * len(counts.columns))  # noqa: F841
+        test_bools = pd.DataFrame({"entrez_gene_ids": entrez_ids})
+        for i in range(len(counts_per_million.columns)):
+            cutoff = (
+                10e6 / (np.median(np.sum(counts[:, i])))
+                if cut_off == "default"
+                else 1e6 * cut_off / np.median(np.sum(counts[:, i]))
+            )
+            test_bools = test_bools.merge(counts_per_million[counts_per_million.iloc[:, i] > cutoff])
+
+    return metrics
+
 def zfpkm_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions, calcualte_fpkm: bool) -> NamedMetrics:
     """Apply zFPKM filtering to the FPKM matrix for a given sample."""
     min_sample_expression = filtering_options.replicate_ratio

From b6460c22de205a9932872e19806b062f5d05aeeb Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 15:15:35 -0600
Subject: [PATCH 058/345] feat: added tpm quantile filtering

---
 main/como/rnaseq_gen.py | 48 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 48 insertions(+)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 4d04dcef..6ec871e3 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -536,6 +536,54 @@ def cpm_filter(
 
     return metrics
 
+
+def tpm_quantile_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions) -> NamedMetrics:
+    """Apply quantile-based filtering to the TPM matrix for a given sample."""
+    # TODO: Write the TPM matrix to disk
+
+    n_exp = filtering_options.replicate_ratio
+    n_top = filtering_options.high_replicate_ratio
+    cut_off = filtering_options.cut_off
+    metrics = calculate_tpm(metrics)
+
+    sample: str
+    metric: _StudyMetrics
+    for sample, metric in metrics.items():
+        entrez_ids = metric.entrez_gene_ids
+        gene_size = metric.gene_sizes
+        tpm_matrix: pd.DataFrame = metric.normalization_matrix
+
+        min_samples = round(n_exp * len(tpm_matrix.columns))
+        top_samples = round(n_top * len(tpm_matrix.columns))
+
+        tpm_quantile = tpm_matrix[tpm_matrix > 0]
+        quantile_cutoff = np.quantile(
+            a=tpm_quantile.values, q=1 - (cut_off / 100), axis=0
+        )  # Compute quantile across columns
+        boolean_expression = pd.DataFrame(
+            data=tpm_matrix > quantile_cutoff, index=tpm_matrix.index, columns=tpm_matrix.columns
+        ).astype(int)
+
+        min_func = k_over_a(min_samples, 0.9)
+        top_func = k_over_a(top_samples, 0.9)
+
+        min_genes: npt.NDArray[bool] = genefilter(boolean_expression, min_func)
+        top_genes: npt.NDArray[bool] = genefilter(boolean_expression, top_func)
+
+        # Only keep `entrez_gene_ids` that pass `min_genes`
+        metric.entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, min_genes) if keep]
+        metric.gene_sizes = [gene for gene, keep in zip(gene_size, min_genes) if keep]
+        metric.count_matrix = metric.count_matrix.iloc[min_genes, :]
+        metric.normalization_matrix = metrics[sample].normalization_matrix.iloc[min_genes, :]
+
+        keep_top_genes = [gene for gene, keep in zip(entrez_ids, top_genes) if keep]
+        metric.high_confidence_entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, keep_top_genes) if keep]
+
+    metrics = calculate_z_score(metrics)
+
+    return metrics
+
+
 def zfpkm_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions, calcualte_fpkm: bool) -> NamedMetrics:
     """Apply zFPKM filtering to the FPKM matrix for a given sample."""
     min_sample_expression = filtering_options.replicate_ratio

From e0c98b430e4b1c14801b131efdfd406b8a36d470 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 15:15:53 -0600
Subject: [PATCH 059/345] feat: added root filtering logic

---
 main/como/rnaseq_gen.py | 26 +++++++++++++++++++++++---
 1 file changed, 23 insertions(+), 3 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 6ec871e3..745d519b 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -619,9 +619,29 @@ def zfpkm_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions,
     return metrics
 
 
-async def rnaseq_gen(
-    # config_filepath: Path,
-    config_filename: str,
+def filter_counts(
+    *,
+    context_name: str,
+    metrics: NamedMetrics,
+    technique: FilteringTechnique,
+    filtering_options: _FilteringOptions,
+    prep: RNAPrepMethod,
+) -> NamedMetrics:
+    """Filter the count matrix based on the specified technique."""
+    match technique:
+        case FilteringTechnique.cpm:
+            return cpm_filter(
+                context_name=context_name, metrics=metrics, filtering_options=filtering_options, prep=prep
+            )
+        case FilteringTechnique.tpm:
+            return tpm_quantile_filter(metrics=metrics, filtering_options=filtering_options)
+        case FilteringTechnique.zfpkm:
+            return zfpkm_filter(metrics=metrics, filtering_options=filtering_options, calcualte_fpkm=True)
+        case FilteringTechnique.umi:
+            return zfpkm_filter(metrics=metrics, filtering_options=filtering_options, calcualte_fpkm=False)
+        case _:
+            raise ValueError(f"Technique must be one of {FilteringTechnique}")
+
     prep: RNAPrepMethod,
     taxon_id: int | str | Taxon,
     replicate_ratio: float = 0.5,

From a4d6f2f94099639b8fea828d06e6b3f5ab9545e5 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 15:16:15 -0600
Subject: [PATCH 060/345] feat: added logic for performing statistical tests

---
 main/como/rnaseq_gen.py | 76 ++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 75 insertions(+), 1 deletion(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 745d519b..b430c916 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -642,8 +642,82 @@ def filter_counts(
         case _:
             raise ValueError(f"Technique must be one of {FilteringTechnique}")
 
+
+async def _save_rnaseq_tests(
+    context_name: str,
+    rnaseq_matrix: pd.DataFrame,
+    metadata_df: pd.DataFrame,
+    gene_info_df: pd.DataFrame,
+    output_filepath: Path,
     prep: RNAPrepMethod,
-    taxon_id: int | str | Taxon,
+    taxon: int,
+    replicate_ratio: float,
+    batch_ratio: float,
+    high_replicate_ratio: float,
+    high_batch_ratio: float,
+    technique: FilteringTechnique,
+    cut_off: int | float,
+):
+    """Save the results of the RNA-Seq tests to a CSV file."""
+    filtering_options = _FilteringOptions(
+        replicate_ratio=replicate_ratio,
+        batch_ratio=batch_ratio,
+        cut_off=cut_off,
+        high_replicate_ratio=high_replicate_ratio,
+        high_batch_ratio=high_batch_ratio,
+    )
+
+    read_counts_results: _ReadMatrixResults = await _build_matrix_results(
+        matrix=rnaseq_matrix,
+        gene_info=gene_info_df,
+        metadata_df=metadata_df,
+        taxon=taxon,
+    )
+    metrics = read_counts_results.metrics
+    entrez_gene_ids = read_counts_results.entrez_gene_ids
+
+    metrics = filter_counts(
+        context_name=context_name,
+        metrics=metrics,
+        technique=technique,
+        filtering_options=filtering_options,
+        prep=prep,
+    )
+
+    expressed_genes: list[str] = []
+    top_genes: list[str] = []
+    for metric in metrics.values():
+        expressed_genes.extend(metric.entrez_gene_ids)
+        top_genes.extend(metric.high_confidence_entrez_gene_ids)
+
+    expression_frequency = pd.Series(expressed_genes).value_counts()
+    expression_df = pd.DataFrame(
+        {"entrez_gene_id": expression_frequency.index, "frequency": expression_frequency.values}
+    )
+    expression_df["prop"] = expression_df["frequency"] / len(metrics)
+    expression_df = expression_df[expression_df["prop"] >= filtering_options.batch_ratio]
+
+    top_frequency = pd.Series(top_genes).value_counts()
+    top_df = pd.DataFrame({"entrez_gene_id": top_frequency.index, "frequency": top_frequency.values})
+    top_df["prop"] = top_df["frequency"] / len(metrics)
+    top_df = top_df[top_df["prop"] >= filtering_options.high_batch_ratio]
+
+    boolean_matrix = pd.DataFrame(data={"entrez_gene_id": entrez_gene_ids, "expressed": 0, "high": 0})
+    for gene in entrez_gene_ids:
+        if gene in expression_df["entrez_gene_id"]:
+            boolean_matrix.loc[gene, "expressed"] = 1
+        if gene in top_df["entrez_gene_id"]:
+            boolean_matrix.loc[gene, "high"] = 1
+
+    expressed_count = len(boolean_matrix[boolean_matrix["expressed"] == 1])
+    high_confidence_count = len(boolean_matrix[boolean_matrix["high"] == 1])
+
+    boolean_matrix.to_csv(output_filepath, index=False)
+    logger.info(
+        f"{context_name} - Found {expressed_count} expressed and {high_confidence_count} confidently expressed genes"
+    )
+    logger.success(f"Wrote boolean matrix to {output_filepath}")
+
     replicate_ratio: float = 0.5,
     high_replicate_ratio: float = 1.0,
     batch_ratio: float = 0.5,

From e9665bf168d7d5724b748830040041db96bee79c Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 15:16:29 -0600
Subject: [PATCH 061/345] feat: create metadata df

---
 main/como/rnaseq_gen.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index b430c916..f86b1b17 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -718,6 +718,15 @@ async def _save_rnaseq_tests(
     )
     logger.success(f"Wrote boolean matrix to {output_filepath}")
 
+
+async def _create_metadata_df(path: Path) -> pd.DataFrame:
+    if path.suffix not in {".xls", ".xlsx"}:
+        raise ValueError(
+            f"Expected an excel file with extension of '.xlsx' or '.xls', got '{path.suffix}'. "
+            f"Attempted to process: {path}"
+        )
+    return pd.read_excel(path)
+
     replicate_ratio: float = 0.5,
     high_replicate_ratio: float = 1.0,
     batch_ratio: float = 0.5,

From dc7c1fec6ec09beb8712676ad2e808a860525fbc Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 15:16:54 -0600
Subject: [PATCH 062/345] refactor: allow passing specific filepaths

---
 main/como/rnaseq_gen.py | 65 +++++++++++++++++++++++++++++------------
 1 file changed, 46 insertions(+), 19 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index f86b1b17..13619059 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -727,6 +727,16 @@ async def _create_metadata_df(path: Path) -> pd.DataFrame:
         )
     return pd.read_excel(path)
 
+
+async def rnaseq_gen(  # noqa: C901, allow complex function
+    context_name: str,
+    input_rnaseq_filepath: Path,
+    input_gene_info_filepath: Path,
+    output_rnaseq_filepath: Path,
+    prep: RNAPrepMethod,
+    taxon: int,
+    input_metadata_filepath: Path | None = None,
+    input_metadata_df: pd.DataFrame | None = None,
     replicate_ratio: float = 0.5,
     high_replicate_ratio: float = 1.0,
     batch_ratio: float = 0.5,
@@ -740,9 +750,9 @@ async def _create_metadata_df(path: Path) -> pd.DataFrame:
         then study/batch numbers are checked for consensus according to batch ratios.
     The zFPKM method is outlined here: https://pubmed.ncbi.nlm.nih.gov/24215113/
 
-    :param config_filename: The configuration filename to read
+    :param metadata_filepath: The configuration filename to read
     :param prep: The preparation method
-    :param taxon_id: The NCBI Taxon ID
+    :param taxon: The NCBI Taxon ID
     :param replicate_ratio: The percentage of replicates that a gene must
         appear in for a gene to be marked as "active" in a batch/study
     :param batch_ratio: The percentage of batches that a gene must appear in for a gene to be marked as 'active"
@@ -754,41 +764,58 @@ async def _create_metadata_df(path: Path) -> pd.DataFrame:
     :param cut_off: The cutoff value to use for the provided filtering technique
     :return: None
     """
-    if isinstance(technique, str):
-        technique = FilteringTechnique(technique.lower())
-    if isinstance(taxon_id, (str, int)):
-        taxon_id = Taxon.from_string(str(taxon_id))
+    if not input_metadata_df and not input_metadata_filepath:
+        raise ValueError("At least one of input_metadata_filepath or input_metadata_df must be provided")
+
+    technique = (
+        FilteringTechnique.from_string(str(technique.lower())) if isinstance(technique, (str, int)) else technique
+    )
 
     match technique:
         case FilteringTechnique.tpm:
-            cut_off = 25 if cut_off is None else cut_off
+            cut_off = cut_off or 25
             if cut_off < 1 or cut_off > 100:
                 raise ValueError("Quantile must be between 1 - 100")
 
         case FilteringTechnique.cpm:
-            if cut_off is not None and cut_off < 0:
+            if cut_off and cut_off < 0:
                 raise ValueError("Cutoff must be greater than 0")
-            elif cut_off is None:
+            elif cut_off:
                 cut_off = "default"
 
         case FilteringTechnique.zfpkm:
-            cut_off = "default" if cut_off is None else cut_off
+            cut_off = "default" if cut_off else cut_off
         case FilteringTechnique.umi:
             pass
         case _:
             raise ValueError(f"Technique must be one of {FilteringTechnique}")
 
-    await _handle_context_batch(
-        config_filename=config_filename,
+    if not input_rnaseq_filepath.exists():
+        raise FileNotFoundError(f"Input RNA-seq file not found! Searching for: '{input_rnaseq_filepath}'")
+
+    if prep == RNAPrepMethod.SCRNA:
+        technique = FilteringTechnique.umi
+        logger.warning(
+            "Single cell filtration does not normalize and assumes "
+            "gene counts are counted with Unique Molecular Identifiers (UMIs). "
+            "Setting filtering technique to UMI now."
+        )
+
+    logger.debug(f"Starting '{context_name}'")
+    output_rnaseq_filepath.parent.mkdir(parents=True, exist_ok=True)
+
+    await _save_rnaseq_tests(
+        context_name=context_name,
+        rnaseq_matrix=await _read_counts(input_rnaseq_filepath),
+        metadata_df=input_metadata_df or await _create_metadata_df(input_metadata_filepath),
+        gene_info_df=pd.read_csv(input_gene_info_filepath),
+        output_filepath=output_rnaseq_filepath,
+        prep=prep,
+        taxon=taxon,
         replicate_ratio=replicate_ratio,
-        replicate_ratio_high=high_replicate_ratio,
         batch_ratio=batch_ratio,
-        batch_ratio_high=high_batch_ratio,
+        high_replicate_ratio=high_replicate_ratio,
+        high_batch_ratio=high_batch_ratio,
         technique=technique,
         cut_off=cut_off,
-        prep=prep,
-        taxon=taxon_id,
-    )
-
-
     )

From f35e09a263ee552d42e188ab71e2c5c52dc686c2 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 9 Dec 2024 15:17:25 -0600
Subject: [PATCH 063/345] refactor: rename variable names for easier reuse

---
 main/COMO.ipynb | 23 ++++++++++++++---------
 1 file changed, 14 insertions(+), 9 deletions(-)

diff --git a/main/COMO.ipynb b/main/COMO.ipynb
index d361f9b2..b721bbbb 100644
--- a/main/COMO.ipynb
+++ b/main/COMO.ipynb
@@ -276,22 +276,22 @@
     "from como.types import RNAPrepMethod\n",
     "\n",
     "context_names = [\"naiveB\"]\n",
-    "output_gene_info_filepaths = [Path(f\"data/results/{context}/gene_info.csv\") for context in context_names]\n",
-    "como_context_dirs = [Path(f\"data/COMO_input/{context}\") for context in context_names]\n",
-    "output_trna_filepaths = [Path(f\"data/results/{context}/total-rna/totalrna_{context}.csv\") for context in context_names]\n",
-    "output_polya_filepaths = [Path(f\"data/results/{context}/polya-rna/polyarna_{context}.csv\") for context in context_names]\n",
+    "gene_info_filepath = [Path(f\"data/results/{context}/gene_info.csv\") for context in context_names]\n",
+    "como_context_dir = [Path(f\"data/COMO_input/{context}\") for context in context_names]\n",
+    "trna_matrix_filepath = [Path(f\"data/results/{context}/total-rna/totalrna_{context}.csv\") for context in context_names]\n",
+    "polya_matrix_filepath = [Path(f\"data/results/{context}/polya-rna/polyarna_{context}.csv\") for context in context_names]\n",
     "\n",
     "\n",
     "for i in range(len(context_names)):\n",
     "    await rnaseq_preprocess(\n",
     "        context_name=context_names[i],\n",
     "        taxon=9606,\n",
-    "        output_gene_info_filepath=output_gene_info_filepaths[i],\n",
-    "        como_context_dir=como_context_dirs[i],\n",
+    "        output_gene_info_filepath=gene_info_filepath[i],\n",
+    "        como_context_dir=como_context_dir[i],\n",
     "        output_trna_config_filepath=Path(\"./data/config_sheets/trna_config.xlsx\"),\n",
-    "        output_trna_count_matrix_filepath=output_trna_filepaths[i],\n",
+    "        output_trna_count_matrix_filepath=trna_matrix_filepath[i],\n",
     "        output_polya_config_filepath=Path(\"./data/config_sheets/mrna_config.xlsx\"),\n",
-    "        output_polya_count_matrix_filepath=output_polya_filepaths[i],\n",
+    "        output_polya_count_matrix_filepath=polya_matrix_filepath[i],\n",
     "        cache=True,\n",
     "        log_level=\"INFO\",\n",
     "    )"
@@ -373,7 +373,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# step 2.2 RNA-seq Analysis for Total RNA-seq library preparation\n",
+    "from como.rnaseq_gen import rnaseq_gen\n",
     "\n",
     "trnaseq_config_file = \"trnaseq_data_inputs_auto.xlsx\"\n",
     "rep_ratio = 0.75\n",
@@ -438,6 +438,11 @@
     "minimum_cutoff = -3\n",
     "taxon_id = \"human\"\n",
     "\n",
+    "await rnaseq_gen(\n",
+    "    context_name=\"naiveB\",\n",
+    "    input_rnaseq_filepath=\n",
+    ")\n",
+    "\n",
     "# fmt: off\n",
     "cmd = \" \".join(\n",
     "    [\n",

From fc803bcc5298023bd563b29a1547fd488c9e4944 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 10 Dec 2024 10:17:35 -0600
Subject: [PATCH 064/345] style: update log message, more pythonic code

---
 main/COMO.ipynb                | 195 +++++++++++++++++----------------
 main/como/rnaseq_preprocess.py |   8 +-
 2 files changed, 104 insertions(+), 99 deletions(-)

diff --git a/main/COMO.ipynb b/main/COMO.ipynb
index b721bbbb..24d4b83c 100644
--- a/main/COMO.ipynb
+++ b/main/COMO.ipynb
@@ -223,6 +223,32 @@
     "</table>"
    ]
   },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-09T22:01:53.960204Z",
+     "start_time": "2024-12-09T22:01:53.957844Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "taxon_id = 9606\n",
+    "context_names = [\"naiveB\"]\n",
+    "gene_info_filepath = [Path(f\"data/results/{context}/gene_info.csv\") for context in context_names]\n",
+    "como_context_dir = [Path(f\"data/COMO_input/{context}\") for context in context_names]\n",
+    "trna_matrix_filepath = [Path(f\"data/results/{context}/trna-rna/trna_{context}.csv\") for context in context_names]\n",
+    "polya_matrix_filepath = [Path(f\"data/results/{context}/polya-rna/polyarna_{context}.csv\") for context in context_names]\n",
+    "\n",
+    "# No single-cell data is provided by default; COMO accepts single-cell data in CSV or h5ad format\n",
+    "# If you are using single-cell data, adjust the following lines accordingly\n",
+    "scrna_matrix_filepath = [Path(f\"data/results/{context}/scrna/scrna_{context}.csv\") for context in context_names]\n",
+    "# scrna_matrix_filepath = [Path(f\"data/results/{context}/scrna/scrna_{context}.h5ad\") for context in context_names]\n"
+   ],
+   "outputs": [],
+   "execution_count": 3
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -270,27 +296,19 @@
     }
    ],
    "source": [
-    "from pathlib import Path\n",
-    "\n",
     "from como.rnaseq_preprocess import rnaseq_preprocess\n",
     "from como.types import RNAPrepMethod\n",
     "\n",
-    "context_names = [\"naiveB\"]\n",
-    "gene_info_filepath = [Path(f\"data/results/{context}/gene_info.csv\") for context in context_names]\n",
-    "como_context_dir = [Path(f\"data/COMO_input/{context}\") for context in context_names]\n",
-    "trna_matrix_filepath = [Path(f\"data/results/{context}/total-rna/totalrna_{context}.csv\") for context in context_names]\n",
-    "polya_matrix_filepath = [Path(f\"data/results/{context}/polya-rna/polyarna_{context}.csv\") for context in context_names]\n",
-    "\n",
     "\n",
     "for i in range(len(context_names)):\n",
     "    await rnaseq_preprocess(\n",
     "        context_name=context_names[i],\n",
-    "        taxon=9606,\n",
+    "        taxon=taxon_id,\n",
     "        output_gene_info_filepath=gene_info_filepath[i],\n",
     "        como_context_dir=como_context_dir[i],\n",
     "        output_trna_config_filepath=Path(\"./data/config_sheets/trna_config.xlsx\"),\n",
     "        output_trna_count_matrix_filepath=trna_matrix_filepath[i],\n",
-    "        output_polya_config_filepath=Path(\"./data/config_sheets/mrna_config.xlsx\"),\n",
+    "        output_polya_config_filepath=Path(\"./data/config_sheets/polya_config.xlsx\"),\n",
     "        output_polya_count_matrix_filepath=polya_matrix_filepath[i],\n",
     "        cache=True,\n",
     "        log_level=\"INFO\",\n",
@@ -373,35 +391,31 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from como.rnaseq_gen import rnaseq_gen\n",
+    "from como.rnaseq_gen import rnaseq_gen, FilteringTechnique\n",
     "\n",
-    "trnaseq_config_file = \"trnaseq_data_inputs_auto.xlsx\"\n",
-    "rep_ratio = 0.75\n",
-    "group_ratio = 0.75\n",
-    "rep_ratio_h = 1.0\n",
-    "group_ratio_h = 1.0\n",
-    "technique = \"zFPKM\"\n",
-    "minimum_cutoff = -3\n",
-    "taxon_id = \"human\"\n",
-    "\n",
-    "# fmt: off\n",
-    "cmd = \" \".join(\n",
-    "    [\n",
-    "        \"python3\", \"como/rnaseq_gen.py\",\n",
-    "        \"--config-file\", trnaseq_config_file,\n",
-    "        \"--replicate-ratio\", str(rep_ratio),\n",
-    "        \"--batch-ratio\", str(group_ratio),\n",
-    "        \"--high-replicate-ratio\", str(rep_ratio_h),\n",
-    "        \"--high-batch-ratio\", str(group_ratio_h),\n",
-    "        \"--minimum-cutoff\", str(minimum_cutoff),\n",
-    "        \"--filt-technique\", f\"{technique}\",\n",
-    "        \"--library-prep\", \"total\",\n",
-    "        \"--taxon-id\", taxon_id\n",
-    "    ]\n",
-    ")\n",
-    "# fmt: on\n",
-    "\n",
-    "!{cmd}"
+    "replicate_ratio = 0.75\n",
+    "high_confidence_replicate_ratio = 1.0\n",
+    "batch_ratio = 0.75\n",
+    "high_confidence_batch_ratio = 1.0\n",
+    "technique = FilteringTechnique.zfpkm\n",
+    "cutoff = -3\n",
+    "\n",
+    "for i, context in enumerate(context_names):\n",
+    "    await rnaseq_gen(\n",
+    "        context_name=context,\n",
+    "        input_rnaseq_filepath=trna_matrix_filepath[i],\n",
+    "        input_gene_info_filepath=gene_info_filepath[i],\n",
+    "        output_rnaseq_filepath=trna_matrix_filepath[i],\n",
+    "        prep=RNAPrepMethod.TOTAL,\n",
+    "        taxon=taxon_id,\n",
+    "        input_metadata_filepath=Path(\"./data/config_sheets/trna_config.xlsx\"),\n",
+    "        replicate_ratio=replicate_ratio,\n",
+    "        high_replicate_ratio=high_confidence_replicate_ratio,\n",
+    "        batch_ratio=batch_ratio,\n",
+    "        high_batch_ratio=high_confidence_batch_ratio,\n",
+    "        technique=technique,\n",
+    "        cutoff=cutoff\n",
+    "    )"
    ]
   },
   {
@@ -429,38 +443,31 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mrnaseq_config_file = \"mrnaseq_data_inputs_auto.xlsx\"\n",
-    "rep_ratio = 0.75\n",
-    "group_ratio = 0.75\n",
-    "rep_ratio_h = 1.0\n",
-    "group_ratio_h = 1.0\n",
-    "technique = \"zfpkm\"\n",
-    "minimum_cutoff = -3\n",
-    "taxon_id = \"human\"\n",
-    "\n",
-    "await rnaseq_gen(\n",
-    "    context_name=\"naiveB\",\n",
-    "    input_rnaseq_filepath=\n",
-    ")\n",
+    "from como.rnaseq_gen import rnaseq_gen, FilteringTechnique\n",
     "\n",
-    "# fmt: off\n",
-    "cmd = \" \".join(\n",
-    "    [\n",
-    "        \"python3\", \"como/rnaseq_gen.py\",\n",
-    "        \"--config-file\", mrnaseq_config_file,\n",
-    "        \"--replicate-ratio\", str(rep_ratio),\n",
-    "        \"--batch-ratio\", str(group_ratio),\n",
-    "        \"--high-replicate-ratio\", str(rep_ratio_h),\n",
-    "        \"--high-batch-ratio\", str(group_ratio_h),\n",
-    "        \"--minimum-cutoff\", str(minimum_cutoff),\n",
-    "        \"--filt-technique\", f\"{technique}\",\n",
-    "        \"--library-prep\", \"mrna\",\n",
-    "        \"--taxon-id\", taxon_id\n",
-    "    ]\n",
-    ")\n",
-    "# fmt: on\n",
-    "\n",
-    "!{cmd}"
+    "replicate_ratio = 0.75\n",
+    "high_confidence_replicate_ratio = 1.0\n",
+    "batch_ratio = 0.75\n",
+    "high_confidence_batch_ratio = 1.0\n",
+    "technique = FilteringTechnique.zfpkm\n",
+    "cutoff = -3\n",
+    "\n",
+    "for i, context in enumerate(context_names):\n",
+    "    await rnaseq_gen(\n",
+    "        context_name=context,\n",
+    "        input_rnaseq_filepath=polya_matrix_filepath[i],\n",
+    "        input_gene_info_filepath=gene_info_filepath[i],\n",
+    "        output_rnaseq_filepath=polya_matrix_filepath[i],\n",
+    "        prep=RNAPrepMethod.MRNA,\n",
+    "        taxon=taxon_id,\n",
+    "        input_metadata_filepath=Path(\"./data/config_sheets/mrna_config.xlsx\"),\n",
+    "        replicate_ratio=replicate_ratio,\n",
+    "        high_replicate_ratio=high_confidence_replicate_ratio,\n",
+    "        batch_ratio=batch_ratio,\n",
+    "        high_batch_ratio=high_confidence_batch_ratio,\n",
+    "        technique=technique,\n",
+    "        cutoff=cutoff\n",
+    "    )"
    ]
   },
   {
@@ -488,33 +495,31 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "scrnaseq_config_file = \"scrnaseq_data_inputs_auto.xlsx\"\n",
-    "rep_ratio = 0.75\n",
-    "group_ratio = 0.75\n",
-    "rep_ratio_h = 1.0\n",
-    "group_ratio_h = 1.0\n",
-    "quantile = 50\n",
-    "minimum_cutoff = -3\n",
-    "taxon_id = \"human\"\n",
-    "\n",
-    "# fmt: off\n",
-    "cmd = \" \".join(\n",
-    "    [\n",
-    "        \"python3\", \"como/rnaseq_gen.py\",\n",
-    "        \"--config-file\", scrnaseq_config_file,\n",
-    "        \"--replicate-ratio\", str(rep_ratio),\n",
-    "        \"--batch-ratio\", str(group_ratio),\n",
-    "        \"--high-replicate-ratio\", str(rep_ratio_h),\n",
-    "        \"--high-batch-ratio\", str(group_ratio_h),\n",
-    "        \"--minimum-cutoff\", str(minimum_cutoff),\n",
-    "        \"--filt-technique\", \"umi\",\n",
-    "        \"--library-prep\", \"scrna\",\n",
-    "        \"--taxon-id\", taxon_id\n",
-    "    ]\n",
-    ")\n",
-    "# fmt: on\n",
+    "from como.rnaseq_gen import rnaseq_gen, FilteringTechnique\n",
     "\n",
-    "!{cmd}"
+    "replicate_ratio = 0.75\n",
+    "high_confidence_replicate_ratio = 1.0\n",
+    "batch_ratio = 0.75\n",
+    "high_confidence_batch_ratio = 1.0\n",
+    "technique = FilteringTechnique.umi\n",
+    "cutoff = -3\n",
+    "\n",
+    "for i, context in enumerate(context_names):\n",
+    "    await rnaseq_gen(\n",
+    "        context_name=context,\n",
+    "        input_rnaseq_filepath=scrna_matrix_filepath[i],\n",
+    "        input_gene_info_filepath=gene_info_filepath[i],\n",
+    "        output_rnaseq_filepath=scrna_matrix_filepath[i],\n",
+    "        prep=RNAPrepMethod.SCRNA,\n",
+    "        taxon=taxon_id,\n",
+    "        input_metadata_filepath=Path(\"./data/config_sheets/scrna_config.xlsx\"),\n",
+    "        replicate_ratio=replicate_ratio,\n",
+    "        high_replicate_ratio=high_confidence_replicate_ratio,\n",
+    "        batch_ratio=batch_ratio,\n",
+    "        high_batch_ratio=high_confidence_batch_ratio,\n",
+    "        technique=technique,\n",
+    "        cutoff=cutoff\n",
+    "    )"
    ]
   },
   {
diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index ae8bcdea..bfa19bc7 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -448,11 +448,11 @@ async def read_counts(file: Path) -> list[str]:
         )
         return conversion["entrez_gene_id"].tolist()
 
-    logger.info("Fetching gene info (this may take 1-5 minutes)")
+    logger.info(
+        "Fetching gene info (this may take 1-5 minutes depending on the number of genes and your internet connection)"
+    )
     genes = set(chain.from_iterable(await asyncio.gather(*[read_counts(f) for f in counts_matrix_filepaths])))
-
-    mygene = MyGene(cache=cache)
-    gene_data = await mygene.query(items=list(genes), taxon=taxon, scopes="entrezgene")
+    gene_data = await MyGene(cache=cache).query(items=list(genes), taxon=taxon, scopes="entrezgene")
     gene_info: pd.DataFrame = pd.DataFrame(
         data=None,
         columns=pd.Index(data=["ensembl_gene_id", "gene_symbol", "entrez_gene_id", "start_position", "end_position"]),

From 8bdddd911680e2ab4a2865aef8dfef25c6707ba0 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 10 Dec 2024 10:17:49 -0600
Subject: [PATCH 065/345] style: variable rename

---
 main/como/rnaseq_gen.py | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 13619059..05f185f3 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -742,7 +742,7 @@ async def rnaseq_gen(  # noqa: C901, allow complex function
     batch_ratio: float = 0.5,
     high_batch_ratio: float = 1.0,
     technique: FilteringTechnique | str = FilteringTechnique.tpm,
-    cut_off: int | float | None = None,
+    cutoff: int | float | None = None,
 ) -> None:
     """Generate a list of active and high-confidence genes from a gene count matrix.
 
@@ -761,7 +761,7 @@ async def rnaseq_gen(  # noqa: C901, allow complex function
     :param high_batch_ratio: The percentage of batches that a gene must
         appear in for a gene to be marked "highly confident" in its expression
     :param technique: The filtering technique to use
-    :param cut_off: The cutoff value to use for the provided filtering technique
+    :param cutoff: The cutoff value to use for the provided filtering technique
     :return: None
     """
     if not input_metadata_df and not input_metadata_filepath:
@@ -773,18 +773,18 @@ async def rnaseq_gen(  # noqa: C901, allow complex function
 
     match technique:
         case FilteringTechnique.tpm:
-            cut_off = cut_off or 25
-            if cut_off < 1 or cut_off > 100:
+            cutoff = cutoff or 25
+            if cutoff < 1 or cutoff > 100:
                 raise ValueError("Quantile must be between 1 - 100")
 
         case FilteringTechnique.cpm:
-            if cut_off and cut_off < 0:
+            if cutoff and cutoff < 0:
                 raise ValueError("Cutoff must be greater than 0")
-            elif cut_off:
-                cut_off = "default"
+            elif cutoff:
+                cutoff = "default"
 
         case FilteringTechnique.zfpkm:
-            cut_off = "default" if cut_off else cut_off
+            cutoff = "default" if cutoff else cutoff
         case FilteringTechnique.umi:
             pass
         case _:
@@ -817,5 +817,5 @@ async def rnaseq_gen(  # noqa: C901, allow complex function
         high_replicate_ratio=high_replicate_ratio,
         high_batch_ratio=high_batch_ratio,
         technique=technique,
-        cut_off=cut_off,
+        cut_off=cutoff,
     )

From e0d84bee1fbf4ea3a197bdd6af89f67d501d4872 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 10 Dec 2024 10:18:31 -0600
Subject: [PATCH 066/345] feat: update to match new approach

---
 main/COMO.ipynb | 123 ++++++++++++++++++++++++++++++++++--------------
 1 file changed, 87 insertions(+), 36 deletions(-)

diff --git a/main/COMO.ipynb b/main/COMO.ipynb
index 24d4b83c..758d719b 100644
--- a/main/COMO.ipynb
+++ b/main/COMO.ipynb
@@ -2,7 +2,11 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
    "source": [
     "# COMO: Constraint-based Optomization of Metabolic Objectives\n",
     "\n",
@@ -56,7 +60,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
    "source": [
     "# Step 1: Data Preprocessing and Analysis\n",
     "\n",
@@ -224,30 +232,33 @@
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-12-09T22:01:53.960204Z",
-     "start_time": "2024-12-09T22:01:53.957844Z"
+     "end_time": "2024-12-09T22:10:43.421233Z",
+     "start_time": "2024-12-09T22:10:43.418100Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
     "from pathlib import Path\n",
     "\n",
+    "from como.rnaseq_preprocess import rnaseq_preprocess\n",
+    "from como.types import RNAPrepMethod\n",
+    "\n",
     "taxon_id = 9606\n",
     "context_names = [\"naiveB\"]\n",
     "gene_info_filepath = [Path(f\"data/results/{context}/gene_info.csv\") for context in context_names]\n",
     "como_context_dir = [Path(f\"data/COMO_input/{context}\") for context in context_names]\n",
-    "trna_matrix_filepath = [Path(f\"data/results/{context}/trna-rna/trna_{context}.csv\") for context in context_names]\n",
+    "trna_matrix_filepath = [Path(f\"data/results/{context}/total-rna/totalrna_{context}.csv\") for context in context_names]\n",
     "polya_matrix_filepath = [Path(f\"data/results/{context}/polya-rna/polyarna_{context}.csv\") for context in context_names]\n",
     "\n",
     "# No single-cell data is provided by default; COMO accepts single-cell data in CSV or h5ad format\n",
     "# If you are using single-cell data, adjust the following lines accordingly\n",
     "scrna_matrix_filepath = [Path(f\"data/results/{context}/scrna/scrna_{context}.csv\") for context in context_names]\n",
     "# scrna_matrix_filepath = [Path(f\"data/results/{context}/scrna/scrna_{context}.h5ad\") for context in context_names]\n"
-   ],
-   "outputs": [],
-   "execution_count": 3
+   ]
   },
   {
    "cell_type": "markdown",
@@ -262,7 +273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-12-07T03:30:27.253112Z",
@@ -274,32 +285,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001B[32m2024-12-06 23:12:10\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m629\u001B[0m - \u001B[1mTEST\u001B[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001B[32m2024-12-06 23:12:11\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m274\u001B[0m - \u001B[32m\u001B[1mWrote gene count matrix for 'total' RNA at '/Users/joshl/Projects/COMO/main/data/results/naiveB/total-rna/totalrna_naiveB.csv'\u001B[0m\n",
-      "\u001B[32m2024-12-06 23:12:11\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m274\u001B[0m - \u001B[32m\u001B[1mWrote gene count matrix for 'polya' RNA at '/Users/joshl/Projects/COMO/main/data/results/naiveB/polya-rna/polyarna_naiveB.csv'\u001B[0m\n",
-      "\u001B[32m2024-12-06 23:12:11\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m451\u001B[0m - \u001B[1mFetching gene info (this may take 1-5 minutes)\u001B[0m\n",
-      "\u001B[32m2024-12-06 23:13:04\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m488\u001B[0m - \u001B[32m\u001B[1mGene Info file written at '/Users/joshl/Projects/COMO/main/data/results/naiveB/gene_info.csv'\u001B[0m\n"
+      "\u001b[32m2024-12-09 16:23:55\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mcomo.rnaseq_preprocess\u001b[0m:\u001b[36m274\u001b[0m - \u001b[32m\u001b[1mWrote gene count matrix for 'polya' RNA at '/Users/joshl/Projects/COMO/main/data/results/naiveB/polya-rna/polyarna_naiveB.csv'\u001b[0m\n",
+      "\u001b[32m2024-12-09 16:23:55\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mcomo.rnaseq_preprocess\u001b[0m:\u001b[36m274\u001b[0m - \u001b[32m\u001b[1mWrote gene count matrix for 'total' RNA at '/Users/joshl/Projects/COMO/main/data/results/naiveB/total-rna/totalrna_naiveB.csv'\u001b[0m\n",
+      "\u001b[32m2024-12-09 16:23:55\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mcomo.rnaseq_preprocess\u001b[0m:\u001b[36m451\u001b[0m - \u001b[1mFetching gene info (this may take 1-5 minutes)\u001b[0m\n",
+      "\u001b[32m2024-12-09 16:24:13\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mcomo.rnaseq_preprocess\u001b[0m:\u001b[36m488\u001b[0m - \u001b[32m\u001b[1mGene Info file written at '/Users/joshl/Projects/COMO/main/data/results/naiveB/gene_info.csv'\u001b[0m\n"
      ]
     }
    ],
    "source": [
-    "from como.rnaseq_preprocess import rnaseq_preprocess\n",
-    "from como.types import RNAPrepMethod\n",
-    "\n",
-    "\n",
     "for i in range(len(context_names)):\n",
     "    await rnaseq_preprocess(\n",
     "        context_name=context_names[i],\n",
@@ -387,9 +380,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-09T22:13:42.060657Z",
+     "start_time": "2024-12-09T22:13:41.740347Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2024-12-09 16:18:43.958\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mcomo.rnaseq_gen\u001b[0m:\u001b[36mrnaseq_gen\u001b[0m:\u001b[36m805\u001b[0m - \u001b[34m\u001b[1mStarting 'naiveB'\u001b[0m\n",
+      "\u001b[32m2024-12-09 16:18:43.961\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mcomo.rnaseq_gen\u001b[0m:\u001b[36m_read_counts\u001b[0m:\u001b[36m175\u001b[0m - \u001b[34m\u001b[1mReading CSV file at 'data/results/naiveB/total-rna/totalrna_naiveB.csv'\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       entrez_gene_id  expressed  high\n",
+      "0                7105          0     0\n",
+      "1               64102          0     0\n",
+      "2                8813          0     0\n",
+      "3               57147          0     0\n",
+      "4               55732          0     0\n",
+      "...               ...        ...   ...\n",
+      "34396       124901321          0     0\n",
+      "34397       124902403          0     0\n",
+      "34398       101929614          0     0\n",
+      "34399       107984888          0     0\n",
+      "34400       124900697          0     0\n",
+      "\n",
+      "[34401 rows x 3 columns]\n"
+     ]
+    },
+    {
+     "ename": "KeyError",
+     "evalue": "'ensembl_gene_id'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[0;32m~/Projects/COMO/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'ensembl_gene_id'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[4], line 11\u001b[0m\n\u001b[1;32m      8\u001b[0m cutoff \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m3\u001b[39m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, context \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(context_names):\n\u001b[0;32m---> 11\u001b[0m     \u001b[38;5;28;01mawait\u001b[39;00m rnaseq_gen(  \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m     12\u001b[0m         context_name\u001b[38;5;241m=\u001b[39mcontext,\n\u001b[1;32m     13\u001b[0m         input_rnaseq_filepath\u001b[38;5;241m=\u001b[39mtrna_matrix_filepath[i],\n\u001b[1;32m     14\u001b[0m         input_gene_info_filepath\u001b[38;5;241m=\u001b[39mgene_info_filepath[i],\n\u001b[1;32m     15\u001b[0m         output_rnaseq_filepath\u001b[38;5;241m=\u001b[39mtrna_matrix_filepath[i],\n\u001b[1;32m     16\u001b[0m         prep\u001b[38;5;241m=\u001b[39mRNAPrepMethod\u001b[38;5;241m.\u001b[39mTOTAL,\n\u001b[1;32m     17\u001b[0m         taxon\u001b[38;5;241m=\u001b[39mtaxon_id,\n\u001b[1;32m     18\u001b[0m         input_metadata_filepath\u001b[38;5;241m=\u001b[39mPath(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./data/config_sheets/trna_config.xlsx\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m     19\u001b[0m         replicate_ratio\u001b[38;5;241m=\u001b[39mreplicate_ratio,\n\u001b[1;32m     20\u001b[0m         high_replicate_ratio\u001b[38;5;241m=\u001b[39mhigh_confidence_replicate_ratio,\n\u001b[1;32m     21\u001b[0m         batch_ratio\u001b[38;5;241m=\u001b[39mbatch_ratio,\n\u001b[1;32m     22\u001b[0m         high_batch_ratio\u001b[38;5;241m=\u001b[39mhigh_confidence_batch_ratio,\n\u001b[1;32m     23\u001b[0m         technique\u001b[38;5;241m=\u001b[39mtechnique,\n\u001b[1;32m     24\u001b[0m         cutoff\u001b[38;5;241m=\u001b[39mcutoff\n\u001b[1;32m     25\u001b[0m     )\n",
+      "File \u001b[0;32m~/Projects/COMO/main/como/rnaseq_gen.py:808\u001b[0m, in \u001b[0;36mrnaseq_gen\u001b[0;34m(context_name, input_rnaseq_filepath, input_gene_info_filepath, output_rnaseq_filepath, prep, taxon, input_metadata_filepath, input_metadata_df, replicate_ratio, high_replicate_ratio, batch_ratio, high_batch_ratio, technique, cutoff)\u001b[0m\n\u001b[1;32m    805\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStarting \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcontext_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    806\u001b[0m output_rnaseq_filepath\u001b[38;5;241m.\u001b[39mparent\u001b[38;5;241m.\u001b[39mmkdir(parents\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m--> 808\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m _save_rnaseq_tests(\n\u001b[1;32m    809\u001b[0m     context_name\u001b[38;5;241m=\u001b[39mcontext_name,\n\u001b[1;32m    810\u001b[0m     rnaseq_matrix\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mawait\u001b[39;00m _read_counts(input_rnaseq_filepath),\n\u001b[1;32m    811\u001b[0m     metadata_df\u001b[38;5;241m=\u001b[39minput_metadata_df \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m _create_metadata_df(input_metadata_filepath),\n\u001b[1;32m    812\u001b[0m     gene_info_df\u001b[38;5;241m=\u001b[39mpd\u001b[38;5;241m.\u001b[39mread_csv(input_gene_info_filepath),\n\u001b[1;32m    813\u001b[0m     output_filepath\u001b[38;5;241m=\u001b[39moutput_rnaseq_filepath,\n\u001b[1;32m    814\u001b[0m     prep\u001b[38;5;241m=\u001b[39mprep,\n\u001b[1;32m    815\u001b[0m     taxon\u001b[38;5;241m=\u001b[39mtaxon,\n\u001b[1;32m    816\u001b[0m     replicate_ratio\u001b[38;5;241m=\u001b[39mreplicate_ratio,\n\u001b[1;32m    817\u001b[0m     batch_ratio\u001b[38;5;241m=\u001b[39mbatch_ratio,\n\u001b[1;32m    818\u001b[0m     high_replicate_ratio\u001b[38;5;241m=\u001b[39mhigh_replicate_ratio,\n\u001b[1;32m    819\u001b[0m     high_batch_ratio\u001b[38;5;241m=\u001b[39mhigh_batch_ratio,\n\u001b[1;32m    820\u001b[0m     technique\u001b[38;5;241m=\u001b[39mtechnique,\n\u001b[1;32m    821\u001b[0m     cut_off\u001b[38;5;241m=\u001b[39mcutoff,\n\u001b[1;32m    822\u001b[0m )\n",
+      "File \u001b[0;32m~/Projects/COMO/main/como/rnaseq_gen.py:671\u001b[0m, in \u001b[0;36m_save_rnaseq_tests\u001b[0;34m(context_name, rnaseq_matrix, metadata_df, gene_info_df, output_filepath, prep, taxon, replicate_ratio, batch_ratio, high_replicate_ratio, high_batch_ratio, technique, cut_off)\u001b[0m\n\u001b[1;32m    662\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Save the results of the RNA-Seq tests to a CSV file.\"\"\"\u001b[39;00m\n\u001b[1;32m    663\u001b[0m filtering_options \u001b[38;5;241m=\u001b[39m _FilteringOptions(\n\u001b[1;32m    664\u001b[0m     replicate_ratio\u001b[38;5;241m=\u001b[39mreplicate_ratio,\n\u001b[1;32m    665\u001b[0m     batch_ratio\u001b[38;5;241m=\u001b[39mbatch_ratio,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    668\u001b[0m     high_batch_ratio\u001b[38;5;241m=\u001b[39mhigh_batch_ratio,\n\u001b[1;32m    669\u001b[0m )\n\u001b[0;32m--> 671\u001b[0m read_counts_results: _ReadMatrixResults \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m _build_matrix_results(\n\u001b[1;32m    672\u001b[0m     matrix\u001b[38;5;241m=\u001b[39mrnaseq_matrix,\n\u001b[1;32m    673\u001b[0m     gene_info\u001b[38;5;241m=\u001b[39mgene_info_df,\n\u001b[1;32m    674\u001b[0m     metadata_df\u001b[38;5;241m=\u001b[39mmetadata_df,\n\u001b[1;32m    675\u001b[0m     taxon\u001b[38;5;241m=\u001b[39mtaxon,\n\u001b[1;32m    676\u001b[0m )\n\u001b[1;32m    677\u001b[0m metrics \u001b[38;5;241m=\u001b[39m read_counts_results\u001b[38;5;241m.\u001b[39mmetrics\n\u001b[1;32m    678\u001b[0m entrez_gene_ids \u001b[38;5;241m=\u001b[39m read_counts_results\u001b[38;5;241m.\u001b[39mentrez_gene_ids\n",
+      "File \u001b[0;32m~/Projects/COMO/main/como/rnaseq_gen.py:201\u001b[0m, in \u001b[0;36m_build_matrix_results\u001b[0;34m(matrix, gene_info, metadata_df, taxon)\u001b[0m\n\u001b[1;32m    199\u001b[0m gene_info \u001b[38;5;241m=\u001b[39m gene_info_migrations(gene_info)\n\u001b[1;32m    200\u001b[0m \u001b[38;5;28mprint\u001b[39m(matrix)\n\u001b[0;32m--> 201\u001b[0m conversion \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m ensembl_to_gene_id_and_symbol(ids\u001b[38;5;241m=\u001b[39m\u001b[43mmatrix\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mensembl_gene_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mtolist(), taxon\u001b[38;5;241m=\u001b[39mtaxon)\n\u001b[1;32m    202\u001b[0m matrix \u001b[38;5;241m=\u001b[39m matrix\u001b[38;5;241m.\u001b[39mmerge(conversion, on\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensembl_gene_id\u001b[39m\u001b[38;5;124m\"\u001b[39m, how\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mleft\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    204\u001b[0m \u001b[38;5;66;03m# Only include Entrez and Ensembl Gene IDs that are present in `gene_info`\u001b[39;00m\n",
+      "File \u001b[0;32m~/Projects/COMO/.venv/lib/python3.10/site-packages/pandas/core/frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
+      "File \u001b[0;32m~/Projects/COMO/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m   3810\u001b[0m     ):\n\u001b[1;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'ensembl_gene_id'"
+     ]
+    }
+   ],
    "source": [
     "from como.rnaseq_gen import rnaseq_gen, FilteringTechnique\n",
     "\n",
@@ -401,7 +452,7 @@
     "cutoff = -3\n",
     "\n",
     "for i, context in enumerate(context_names):\n",
-    "    await rnaseq_gen(\n",
+    "    await rnaseq_gen(  # noqa\n",
     "        context_name=context,\n",
     "        input_rnaseq_filepath=trna_matrix_filepath[i],\n",
     "        input_gene_info_filepath=gene_info_filepath[i],\n",

From 967445906d260cff83429a6ef1c1fc43694ef929 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 10 Dec 2024 14:48:32 -0600
Subject: [PATCH 067/345] revert: use mrna instead of polya

---
 main/como/rnaseq_preprocess.py | 26 +++++++++++++-------------
 main/como/types.py             |  2 +-
 2 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index bfa19bc7..d033f752 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -523,7 +523,7 @@ async def _process(
     if output_trna_config_filepath:
         rna_types.append(("total", output_trna_config_filepath, output_trna_matrix_filepath))
     if output_mrna_config_filepath:
-        rna_types.append(("polya", output_mrna_config_filepath, output_mrna_matrix_filepath))
+        rna_types.append(("mrna", output_mrna_config_filepath, output_mrna_matrix_filepath))
 
     # if provided, iterate through como-input specific directories
     tasks = []
@@ -563,9 +563,9 @@ async def rnaseq_preprocess(
     input_matrix_filepath: Path | list[Path] | None = None,
     preparation_method: RNAPrepMethod | list[RNAPrepMethod] | None = None,
     output_trna_config_filepath: Path | None = None,
-    output_polya_config_filepath: Path | None = None,
+    output_mrna_config_filepath: Path | None = None,
     output_trna_count_matrix_filepath: Path | None = None,
-    output_polya_count_matrix_filepath: Path | None = None,
+    output_mrna_count_matrix_filepath: Path | None = None,
     cache: bool = True,
     log_level: Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] = "INFO",
     log_location: str | TextIOWrapper = sys.stderr,
@@ -579,9 +579,9 @@ async def rnaseq_preprocess(
     :param taxon: The NCBI taxonomy ID
     :param output_gene_info_filepath: Path to the output gene information CSV file
     :param output_trna_config_filepath: Path to the output tRNA config file (if in "create" mode)
-    :param output_polya_config_filepath: Path to the output mRNA config file (if in "create" mode)
+    :param output_mrna_config_filepath: Path to the output mRNA config file (if in "create" mode)
     :param output_trna_count_matrix_filepath: The path to write total RNA count matrices
-    :param output_polya_count_matrix_filepath: The path to write messenger RNA count matrices
+    :param output_mrna_count_matrix_filepath: The path to write messenger RNA count matrices
     :param como_context_dir: If in "create" mode, the input path(s) to the COMO_input directory of the current context
         i.e., the directory containing "fragmentSizes", "geneCounts", "insertSizeMetrics", etc. directories
     :param input_matrix_filepath: If in "provide" mode, the path(s) to the count matrices to be processed
@@ -604,18 +604,18 @@ async def rnaseq_preprocess(
     output_trna_config_filepath = (
         output_trna_config_filepath.resolve() if output_trna_config_filepath else output_trna_config_filepath
     )
-    output_polya_config_filepath = (
-        output_polya_config_filepath.resolve() if output_polya_config_filepath else output_polya_config_filepath
+    output_mrna_config_filepath = (
+        output_mrna_config_filepath.resolve() if output_mrna_config_filepath else output_mrna_config_filepath
     )
     output_trna_count_matrix_filepath = (
         output_trna_count_matrix_filepath.resolve()
         if output_trna_count_matrix_filepath
         else output_trna_count_matrix_filepath
     )
-    output_polya_count_matrix_filepath = (
-        output_polya_count_matrix_filepath.resolve()
-        if output_polya_count_matrix_filepath
-        else output_polya_count_matrix_filepath
+    output_mrna_count_matrix_filepath = (
+        output_mrna_count_matrix_filepath.resolve()
+        if output_mrna_count_matrix_filepath
+        else output_mrna_count_matrix_filepath
     )
 
     input_matrix_filepath = _listify(input_matrix_filepath)
@@ -633,8 +633,8 @@ async def rnaseq_preprocess(
         input_matrix_filepath=input_matrix_filepath,
         output_gene_info_filepath=output_gene_info_filepath,
         output_trna_config_filepath=output_trna_config_filepath,
-        output_mrna_config_filepath=output_polya_config_filepath,
+        output_mrna_config_filepath=output_mrna_config_filepath,
         output_trna_matrix_filepath=output_trna_count_matrix_filepath,
-        output_mrna_matrix_filepath=output_polya_count_matrix_filepath,
+        output_mrna_matrix_filepath=output_mrna_count_matrix_filepath,
         cache=cache,
     )
diff --git a/main/como/types.py b/main/como/types.py
index ebe44ecf..42908252 100644
--- a/main/como/types.py
+++ b/main/como/types.py
@@ -47,4 +47,4 @@ def from_string(value: str) -> RNAPrepMethod:
 
 
 type_path = str | Path
-type_rna = Literal["total", "polya"]
+type_rna = Literal["total", "mrna"]

From cde66061b1f410b56f5ada7955870763c8247a7a Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 10 Dec 2024 14:50:40 -0600
Subject: [PATCH 068/345] refactor: check files returned

Instead of checking if the stem of directories match, check that the same number of files are returned. Because the directories are sorted (and this comes from FastqToGeneCounts), they should always match
---
 main/como/rnaseq_preprocess.py | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index d033f752..f7e65040 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -138,16 +138,18 @@ def _organize_gene_counts_files(data_dir: Path) -> list[_StudyMetrics]:
     # For each study, collect gene count files, fragment files, insert size files, layouts, and strandedness information
     study_metrics: list[_StudyMetrics] = []
     for gene_dir, strand_dir in zip(gene_counts_directories, strandedness_directories):
-        if gene_dir.stem != strand_dir.stem:
-            raise ValueError(
-                f"Gene directory name of '{gene_dir.stem}' does not match stranded directory name of '{strand_dir.stem}'"  # noqa: E501
-            )
+        count_files = list(gene_dir.glob("*.tab"))
+        strand_files = list(strand_dir.glob("*.txt"))
+        if len(count_files) == 0:
+            raise ValueError(f"No count files found for study '{gene_dir.stem}'.")
+        if len(strand_files) == 0:
+            raise ValueError(f"No strandedness files found for study '{gene_dir.stem}'.")
 
         study_metrics.append(
             _StudyMetrics(
                 study_name=gene_dir.stem,
-                count_files=list(gene_dir.glob("*.tab")),
-                strand_files=list(strand_dir.glob("*.txt")),
+                count_files=count_files,
+                strand_files=strand_files,
             )
         )
     return study_metrics
@@ -262,6 +264,7 @@ async def _write_counts_matrix(
     counts: list[pd.DataFrame] = await asyncio.gather(
         *[_create_sample_counts_matrix(metric) for metric in study_metrics]
     )
+
     final_matrix = pd.DataFrame()
     for count in counts:
         final_matrix = count if final_matrix.empty else pd.merge(final_matrix, count, on="ensembl_gene_id", how="outer")

From b14d3f9b8c26699346f710c73f3e1cec91327fa5 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 10 Dec 2024 14:51:10 -0600
Subject: [PATCH 069/345] feat: allow specifying specific directories

---
 main/como/rnaseq_preprocess.py | 22 ++++++++++++++++++++--
 1 file changed, 20 insertions(+), 2 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index f7e65040..9da1a5e2 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -278,13 +278,28 @@ async def _write_counts_matrix(
     return final_matrix
 
 
-async def _create_config_df(context_name: str, /, como_input_dir: Path) -> pd.DataFrame:  # noqa: C901
+async def _create_config_df(
+    context_name: str,
+    /,
+    como_context_dir: Path,
+    gene_count_dirname: str = "geneCounts",
+    layout_dirname: str = "layouts",
+    strandedness_dirname: str = "strandedness",
+    fragment_sizes_dirname: str = "fragmentSizes",
+    prep_method_dirname: str = "prepMethods",
+) -> pd.DataFrame:
     """Create configuration sheet.
 
     The configuration file created is based on the gene counts matrix.
      If using zFPKM normalization technique, mean fragment lengths will be fetched
     """
-    gene_counts_files = list(Path(como_input_dir, context_name, "geneCounts").rglob("*.tab"))
+    gene_counts_dir = como_context_dir / gene_count_dirname
+    layout_dir = como_context_dir / layout_dirname
+    strandedness_dir = como_context_dir / strandedness_dirname
+    fragment_sizes_dir = como_context_dir / fragment_sizes_dirname
+    prep_method_dir = como_context_dir / prep_method_dirname
+
+    gene_counts_files = list(gene_counts_dir.rglob("*.tab"))
     sample_names: list[str] = []
     fragment_lengths: list[int | float] = []
     layouts: list[str] = []
@@ -292,6 +307,9 @@ async def _create_config_df(context_name: str, /, como_input_dir: Path) -> pd.Da
     groups: list[str] = []
     preparation_method: list[str] = []
 
+    if len(gene_counts_files) == 0:
+        raise FileNotFoundError(f"No gene count files found in '{gene_counts_dir}'.")
+
     for gene_count_filename in sorted(gene_counts_files):
         try:
             # Match S___R___r___

From d52b0ee1c766d6c15aa31ac98518590f03b3960b Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 10 Dec 2024 14:52:01 -0600
Subject: [PATCH 070/345] style: use more descriptive variable names

---
 main/como/rnaseq_preprocess.py | 22 ++++++++++------------
 1 file changed, 10 insertions(+), 12 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 9da1a5e2..98a283f2 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -311,27 +311,25 @@ async def _create_config_df(
         raise FileNotFoundError(f"No gene count files found in '{gene_counts_dir}'.")
 
     for gene_count_filename in sorted(gene_counts_files):
-        try:
-            # Match S___R___r___
-            # \d{1,3} matches 1-3 digits
-            # (?:r\d{1,3})? matches an option "r" followed by three digits
-            label = re.findall(r"S\d{1,3}R\d{1,3}(?:r\d{1,3})?", gene_count_filename.as_posix())[0]
-
-        except IndexError as e:
-            raise IndexError(
+        # Match S___R___r___
+        # \d{1,3} matches 1-3 digits
+        # (?:r\d{1,3})? optionally matches a "r" followed by three digits
+        label = re.findall(r"S\d{1,3}R\d{1,3}(?:r\d{1,3})?", gene_count_filename.as_posix())[0]
+        if not label:
+            raise ValueError(
                 f"\n\nFilename of '{gene_count_filename}' is not valid. "
                 f"Should be 'contextName_SXRYrZ.tab', where X is the study/batch number, Y is the replicate number, "
                 f"and Z is the run number."
                 "\n\nIf not a multi-run sample, exclude 'rZ' from the filename."
-            ) from e
+            )
 
         study_number = re.findall(r"S\d{1,3}", label)[0]
         rep_number = re.findall(r"R\d{1,3}", label)[0]
-        run = re.findall(r"r\d{1,3}", label)
+        run_number = re.findall(r"r\d{1,3}", label)
 
         multi_flag = 0
-        if len(run) > 0:
-            if run[0] != "r1":
+        if len(run_number) > 0:
+            if run_number[0] != "r1":
                 continue
             else:
                 label_glob = study_number + rep_number + "r*"

From fe1d406c4bd3571394b588a1ead61c64526a4555 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 10 Dec 2024 14:52:35 -0600
Subject: [PATCH 071/345] refactor: use early continue

Removes an extra indentation
---
 main/como/rnaseq_preprocess.py | 32 +++++++++++++++-----------------
 1 file changed, 15 insertions(+), 17 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 98a283f2..0a0c7a35 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -331,23 +331,21 @@ async def _create_config_df(
         if len(run_number) > 0:
             if run_number[0] != "r1":
                 continue
-            else:
-                label_glob = study_number + rep_number + "r*"
-                runs = [run for run in gene_counts_files if re.search(label_glob, run.as_posix())]
-                multi_flag = 1
-                frag_files = []
-
-                for r in runs:
-                    r_label = re.findall(r"r\d{1,3}", r.as_posix())[0]
-                    R_label = re.findall(r"R\d{1,3}", r.as_posix())[0]  # noqa: N806
-                    frag_filename = "".join([context_name, "_", study_number, R_label, r_label, "_fragment_size.txt"])
-                    frag_files.append(como_input_dir / context_name / "fragmentSizes" / study_number / frag_filename)
-
-        context_path = como_input_dir / context_name
-        layout_files: list[Path] = list((context_path / "layouts").rglob(f"{context_name}_{label}_layout.txt"))
-        strand_files: list[Path] = list((context_path / "strandedness").rglob(f"{context_name}_{label}_strandedness.txt"))  # fmt: skip  # noqa: E501
-        frag_files: list[Path] = list((context_path / "fragmentSizes").rglob(f"{context_name}_{label}_fragment_size.txt"))  # fmt: skip  # noqa: E501
-        prep_files: list[Path] = list((context_path / "prepMethods").rglob(f"{context_name}_{label}_prep_method.txt"))
+            label_glob = f"{study_number}{rep_number}r*"  # S__R__r*
+            runs = [run for run in gene_counts_files if re.search(label_glob, run.as_posix())]
+            multi_flag = 1
+            frag_files = []
+
+            for run in runs:
+                run_number = re.findall(r"R\d{1,3}", run.as_posix())[0]
+                replicate = re.findall(r"r\d{1,3}", run.as_posix())[0]
+                frag_filename = "".join([context_name, "_", study_number, run_number, replicate, "_fragment_size.txt"])
+                frag_files.append(como_context_dir / fragment_sizes_dirname / study_number / frag_filename)
+
+        layout_files: list[Path] = list(layout_dir.rglob(f"{context_name}_{label}_layout.txt"))
+        strand_files: list[Path] = list(strandedness_dir.rglob(f"{context_name}_{label}_strandedness.txt"))
+        frag_files: list[Path] = list(fragment_sizes_dir.rglob(f"{context_name}_{label}_fragment_size.txt"))
+        prep_files: list[Path] = list(prep_method_dir.rglob(f"{context_name}_{label}_prep_method.txt"))
 
         layout = "UNKNOWN"
         if len(layout_files) == 0:

From 8f1d02782550c70c23f447f61d7ed9b01906c7e4 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 10 Dec 2024 14:52:50 -0600
Subject: [PATCH 072/345] style: update warning messages

---
 main/como/rnaseq_preprocess.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 0a0c7a35..5c2d8125 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -351,7 +351,7 @@ async def _create_config_df(
         if len(layout_files) == 0:
             logger.warning(
                 f"No layout file found for {label}, writing as 'UNKNOWN', "
-                f"this should be defined by user if using zFPKM or rnaseq_gen.py will not run"
+                f"this should be defined if you are using zFPKM or downstream 'rnaseq_gen.py' will not run"
             )
         elif len(layout_files) == 1:
             with layout_files[0].open("r") as file:
@@ -380,7 +380,7 @@ async def _create_config_df(
 
         prep = "total"
         if len(prep_files) == 0:
-            logger.warning(f"No prep file found for {label}, assuming 'total' as in Total RNA library preparation")
+            logger.warning(f"No prep file found for {label}, assuming 'total', as in 'Total RNA' library preparation")
         elif len(prep_files) == 1:
             with prep_files[0].open("r") as file:
                 prep = file.read().strip().lower()
@@ -393,10 +393,10 @@ async def _create_config_df(
             )
 
         mean_fragment_size = 100
-        if len(frag_files) == 0:
+        if len(frag_files) == 0 and prep != RNAPrepMethod.TOTAL.value:
             logger.warning(
                 f"No fragment file found for {label}, using '100'. "
-                f"This must be defined by the user in order to use zFPKM normalization"
+                "You should define this if you are going to use downstream zFPKM normalization"
             )
         elif len(frag_files) == 1:
             if layout == "single-end":
@@ -512,7 +512,7 @@ async def _create_matrix_file(
     output_counts_matrix_filepath: Path,
     rna: type_rna,
 ) -> None:
-    config_df = await _create_config_df(context_name, como_input_dir=como_context_dir)
+    config_df = await _create_config_df(context_name, como_context_dir=como_context_dir)
     await _write_counts_matrix(
         config_df=config_df,
         como_context_dir=como_context_dir,

From 0ef736e34b5479fa87d17c1db48002a32a38910d Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 10 Dec 2024 14:53:20 -0600
Subject: [PATCH 073/345] style: rename variables

---
 main/COMO.ipynb | 50 ++++++++++++++++++++++++-------------------------
 1 file changed, 25 insertions(+), 25 deletions(-)

diff --git a/main/COMO.ipynb b/main/COMO.ipynb
index 758d719b..52d7fb88 100644
--- a/main/COMO.ipynb
+++ b/main/COMO.ipynb
@@ -233,7 +233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-12-09T22:10:43.421233Z",
@@ -285,10 +285,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2024-12-09 16:23:55\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mcomo.rnaseq_preprocess\u001b[0m:\u001b[36m274\u001b[0m - \u001b[32m\u001b[1mWrote gene count matrix for 'polya' RNA at '/Users/joshl/Projects/COMO/main/data/results/naiveB/polya-rna/polyarna_naiveB.csv'\u001b[0m\n",
-      "\u001b[32m2024-12-09 16:23:55\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mcomo.rnaseq_preprocess\u001b[0m:\u001b[36m274\u001b[0m - \u001b[32m\u001b[1mWrote gene count matrix for 'total' RNA at '/Users/joshl/Projects/COMO/main/data/results/naiveB/total-rna/totalrna_naiveB.csv'\u001b[0m\n",
-      "\u001b[32m2024-12-09 16:23:55\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mcomo.rnaseq_preprocess\u001b[0m:\u001b[36m451\u001b[0m - \u001b[1mFetching gene info (this may take 1-5 minutes)\u001b[0m\n",
-      "\u001b[32m2024-12-09 16:24:13\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mcomo.rnaseq_preprocess\u001b[0m:\u001b[36m488\u001b[0m - \u001b[32m\u001b[1mGene Info file written at '/Users/joshl/Projects/COMO/main/data/results/naiveB/gene_info.csv'\u001b[0m\n"
+      "\u001B[32m2024-12-09 16:23:55\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m274\u001B[0m - \u001B[32m\u001B[1mWrote gene count matrix for 'polya' RNA at '/Users/joshl/Projects/COMO/main/data/results/naiveB/polya-rna/polyarna_naiveB.csv'\u001B[0m\n",
+      "\u001B[32m2024-12-09 16:23:55\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m274\u001B[0m - \u001B[32m\u001B[1mWrote gene count matrix for 'total' RNA at '/Users/joshl/Projects/COMO/main/data/results/naiveB/total-rna/totalrna_naiveB.csv'\u001B[0m\n",
+      "\u001B[32m2024-12-09 16:23:55\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m451\u001B[0m - \u001B[1mFetching gene info (this may take 1-5 minutes)\u001B[0m\n",
+      "\u001B[32m2024-12-09 16:24:13\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m488\u001B[0m - \u001B[32m\u001B[1mGene Info file written at '/Users/joshl/Projects/COMO/main/data/results/naiveB/gene_info.csv'\u001B[0m\n"
      ]
     }
    ],
@@ -301,8 +301,8 @@
     "        como_context_dir=como_context_dir[i],\n",
     "        output_trna_config_filepath=Path(\"./data/config_sheets/trna_config.xlsx\"),\n",
     "        output_trna_count_matrix_filepath=trna_matrix_filepath[i],\n",
-    "        output_polya_config_filepath=Path(\"./data/config_sheets/polya_config.xlsx\"),\n",
-    "        output_polya_count_matrix_filepath=polya_matrix_filepath[i],\n",
+    "        output_mrna_config_filepath=Path(\"./data/config_sheets/polya_config.xlsx\"),\n",
+    "        output_mrna_count_matrix_filepath=polya_matrix_filepath[i],\n",
     "        cache=True,\n",
     "        log_level=\"INFO\",\n",
     "    )"
@@ -392,8 +392,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2024-12-09 16:18:43.958\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mcomo.rnaseq_gen\u001b[0m:\u001b[36mrnaseq_gen\u001b[0m:\u001b[36m805\u001b[0m - \u001b[34m\u001b[1mStarting 'naiveB'\u001b[0m\n",
-      "\u001b[32m2024-12-09 16:18:43.961\u001b[0m | \u001b[34m\u001b[1mDEBUG   \u001b[0m | \u001b[36mcomo.rnaseq_gen\u001b[0m:\u001b[36m_read_counts\u001b[0m:\u001b[36m175\u001b[0m - \u001b[34m\u001b[1mReading CSV file at 'data/results/naiveB/total-rna/totalrna_naiveB.csv'\u001b[0m\n"
+      "\u001B[32m2024-12-09 16:18:43.958\u001B[0m | \u001B[34m\u001B[1mDEBUG   \u001B[0m | \u001B[36mcomo.rnaseq_gen\u001B[0m:\u001B[36mrnaseq_gen\u001B[0m:\u001B[36m805\u001B[0m - \u001B[34m\u001B[1mStarting 'naiveB'\u001B[0m\n",
+      "\u001B[32m2024-12-09 16:18:43.961\u001B[0m | \u001B[34m\u001B[1mDEBUG   \u001B[0m | \u001B[36mcomo.rnaseq_gen\u001B[0m:\u001B[36m_read_counts\u001B[0m:\u001B[36m175\u001B[0m - \u001B[34m\u001B[1mReading CSV file at 'data/results/naiveB/total-rna/totalrna_naiveB.csv'\u001B[0m\n"
      ]
     },
     {
@@ -421,23 +421,23 @@
      "evalue": "'ensembl_gene_id'",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "File \u001b[0;32m~/Projects/COMO/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
-      "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'ensembl_gene_id'",
+      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[0;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
+      "File \u001B[0;32m~/Projects/COMO/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[0;34m(self, key)\u001B[0m\n\u001B[1;32m   3804\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m-> 3805\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_engine\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcasted_key\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   3806\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n",
+      "File \u001B[0;32mindex.pyx:167\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[0;34m()\u001B[0m\n",
+      "File \u001B[0;32mindex.pyx:196\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[0;34m()\u001B[0m\n",
+      "File \u001B[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[0;34m()\u001B[0m\n",
+      "File \u001B[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[0;34m()\u001B[0m\n",
+      "\u001B[0;31mKeyError\u001B[0m: 'ensembl_gene_id'",
       "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[4], line 11\u001b[0m\n\u001b[1;32m      8\u001b[0m cutoff \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m3\u001b[39m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, context \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(context_names):\n\u001b[0;32m---> 11\u001b[0m     \u001b[38;5;28;01mawait\u001b[39;00m rnaseq_gen(  \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m     12\u001b[0m         context_name\u001b[38;5;241m=\u001b[39mcontext,\n\u001b[1;32m     13\u001b[0m         input_rnaseq_filepath\u001b[38;5;241m=\u001b[39mtrna_matrix_filepath[i],\n\u001b[1;32m     14\u001b[0m         input_gene_info_filepath\u001b[38;5;241m=\u001b[39mgene_info_filepath[i],\n\u001b[1;32m     15\u001b[0m         output_rnaseq_filepath\u001b[38;5;241m=\u001b[39mtrna_matrix_filepath[i],\n\u001b[1;32m     16\u001b[0m         prep\u001b[38;5;241m=\u001b[39mRNAPrepMethod\u001b[38;5;241m.\u001b[39mTOTAL,\n\u001b[1;32m     17\u001b[0m         taxon\u001b[38;5;241m=\u001b[39mtaxon_id,\n\u001b[1;32m     18\u001b[0m         input_metadata_filepath\u001b[38;5;241m=\u001b[39mPath(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./data/config_sheets/trna_config.xlsx\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m     19\u001b[0m         replicate_ratio\u001b[38;5;241m=\u001b[39mreplicate_ratio,\n\u001b[1;32m     20\u001b[0m         high_replicate_ratio\u001b[38;5;241m=\u001b[39mhigh_confidence_replicate_ratio,\n\u001b[1;32m     21\u001b[0m         batch_ratio\u001b[38;5;241m=\u001b[39mbatch_ratio,\n\u001b[1;32m     22\u001b[0m         high_batch_ratio\u001b[38;5;241m=\u001b[39mhigh_confidence_batch_ratio,\n\u001b[1;32m     23\u001b[0m         technique\u001b[38;5;241m=\u001b[39mtechnique,\n\u001b[1;32m     24\u001b[0m         cutoff\u001b[38;5;241m=\u001b[39mcutoff\n\u001b[1;32m     25\u001b[0m     )\n",
-      "File \u001b[0;32m~/Projects/COMO/main/como/rnaseq_gen.py:808\u001b[0m, in \u001b[0;36mrnaseq_gen\u001b[0;34m(context_name, input_rnaseq_filepath, input_gene_info_filepath, output_rnaseq_filepath, prep, taxon, input_metadata_filepath, input_metadata_df, replicate_ratio, high_replicate_ratio, batch_ratio, high_batch_ratio, technique, cutoff)\u001b[0m\n\u001b[1;32m    805\u001b[0m logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStarting \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcontext_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    806\u001b[0m output_rnaseq_filepath\u001b[38;5;241m.\u001b[39mparent\u001b[38;5;241m.\u001b[39mmkdir(parents\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m--> 808\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m _save_rnaseq_tests(\n\u001b[1;32m    809\u001b[0m     context_name\u001b[38;5;241m=\u001b[39mcontext_name,\n\u001b[1;32m    810\u001b[0m     rnaseq_matrix\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mawait\u001b[39;00m _read_counts(input_rnaseq_filepath),\n\u001b[1;32m    811\u001b[0m     metadata_df\u001b[38;5;241m=\u001b[39minput_metadata_df \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28;01mawait\u001b[39;00m _create_metadata_df(input_metadata_filepath),\n\u001b[1;32m    812\u001b[0m     gene_info_df\u001b[38;5;241m=\u001b[39mpd\u001b[38;5;241m.\u001b[39mread_csv(input_gene_info_filepath),\n\u001b[1;32m    813\u001b[0m     output_filepath\u001b[38;5;241m=\u001b[39moutput_rnaseq_filepath,\n\u001b[1;32m    814\u001b[0m     prep\u001b[38;5;241m=\u001b[39mprep,\n\u001b[1;32m    815\u001b[0m     taxon\u001b[38;5;241m=\u001b[39mtaxon,\n\u001b[1;32m    816\u001b[0m     replicate_ratio\u001b[38;5;241m=\u001b[39mreplicate_ratio,\n\u001b[1;32m    817\u001b[0m     batch_ratio\u001b[38;5;241m=\u001b[39mbatch_ratio,\n\u001b[1;32m    818\u001b[0m     high_replicate_ratio\u001b[38;5;241m=\u001b[39mhigh_replicate_ratio,\n\u001b[1;32m    819\u001b[0m     high_batch_ratio\u001b[38;5;241m=\u001b[39mhigh_batch_ratio,\n\u001b[1;32m    820\u001b[0m     technique\u001b[38;5;241m=\u001b[39mtechnique,\n\u001b[1;32m    821\u001b[0m     cut_off\u001b[38;5;241m=\u001b[39mcutoff,\n\u001b[1;32m    822\u001b[0m )\n",
-      "File \u001b[0;32m~/Projects/COMO/main/como/rnaseq_gen.py:671\u001b[0m, in \u001b[0;36m_save_rnaseq_tests\u001b[0;34m(context_name, rnaseq_matrix, metadata_df, gene_info_df, output_filepath, prep, taxon, replicate_ratio, batch_ratio, high_replicate_ratio, high_batch_ratio, technique, cut_off)\u001b[0m\n\u001b[1;32m    662\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Save the results of the RNA-Seq tests to a CSV file.\"\"\"\u001b[39;00m\n\u001b[1;32m    663\u001b[0m filtering_options \u001b[38;5;241m=\u001b[39m _FilteringOptions(\n\u001b[1;32m    664\u001b[0m     replicate_ratio\u001b[38;5;241m=\u001b[39mreplicate_ratio,\n\u001b[1;32m    665\u001b[0m     batch_ratio\u001b[38;5;241m=\u001b[39mbatch_ratio,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    668\u001b[0m     high_batch_ratio\u001b[38;5;241m=\u001b[39mhigh_batch_ratio,\n\u001b[1;32m    669\u001b[0m )\n\u001b[0;32m--> 671\u001b[0m read_counts_results: _ReadMatrixResults \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m _build_matrix_results(\n\u001b[1;32m    672\u001b[0m     matrix\u001b[38;5;241m=\u001b[39mrnaseq_matrix,\n\u001b[1;32m    673\u001b[0m     gene_info\u001b[38;5;241m=\u001b[39mgene_info_df,\n\u001b[1;32m    674\u001b[0m     metadata_df\u001b[38;5;241m=\u001b[39mmetadata_df,\n\u001b[1;32m    675\u001b[0m     taxon\u001b[38;5;241m=\u001b[39mtaxon,\n\u001b[1;32m    676\u001b[0m )\n\u001b[1;32m    677\u001b[0m metrics \u001b[38;5;241m=\u001b[39m read_counts_results\u001b[38;5;241m.\u001b[39mmetrics\n\u001b[1;32m    678\u001b[0m entrez_gene_ids \u001b[38;5;241m=\u001b[39m read_counts_results\u001b[38;5;241m.\u001b[39mentrez_gene_ids\n",
-      "File \u001b[0;32m~/Projects/COMO/main/como/rnaseq_gen.py:201\u001b[0m, in \u001b[0;36m_build_matrix_results\u001b[0;34m(matrix, gene_info, metadata_df, taxon)\u001b[0m\n\u001b[1;32m    199\u001b[0m gene_info \u001b[38;5;241m=\u001b[39m gene_info_migrations(gene_info)\n\u001b[1;32m    200\u001b[0m \u001b[38;5;28mprint\u001b[39m(matrix)\n\u001b[0;32m--> 201\u001b[0m conversion \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m ensembl_to_gene_id_and_symbol(ids\u001b[38;5;241m=\u001b[39m\u001b[43mmatrix\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mensembl_gene_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mtolist(), taxon\u001b[38;5;241m=\u001b[39mtaxon)\n\u001b[1;32m    202\u001b[0m matrix \u001b[38;5;241m=\u001b[39m matrix\u001b[38;5;241m.\u001b[39mmerge(conversion, on\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensembl_gene_id\u001b[39m\u001b[38;5;124m\"\u001b[39m, how\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mleft\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    204\u001b[0m \u001b[38;5;66;03m# Only include Entrez and Ensembl Gene IDs that are present in `gene_info`\u001b[39;00m\n",
-      "File \u001b[0;32m~/Projects/COMO/.venv/lib/python3.10/site-packages/pandas/core/frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
-      "File \u001b[0;32m~/Projects/COMO/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m   3810\u001b[0m     ):\n\u001b[1;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'ensembl_gene_id'"
+      "\u001B[0;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
+      "Cell \u001B[0;32mIn[4], line 11\u001B[0m\n\u001B[1;32m      8\u001B[0m cutoff \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m3\u001B[39m\n\u001B[1;32m     10\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i, context \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(context_names):\n\u001B[0;32m---> 11\u001B[0m     \u001B[38;5;28;01mawait\u001B[39;00m rnaseq_gen(  \u001B[38;5;66;03m# noqa\u001B[39;00m\n\u001B[1;32m     12\u001B[0m         context_name\u001B[38;5;241m=\u001B[39mcontext,\n\u001B[1;32m     13\u001B[0m         input_rnaseq_filepath\u001B[38;5;241m=\u001B[39mtrna_matrix_filepath[i],\n\u001B[1;32m     14\u001B[0m         input_gene_info_filepath\u001B[38;5;241m=\u001B[39mgene_info_filepath[i],\n\u001B[1;32m     15\u001B[0m         output_rnaseq_filepath\u001B[38;5;241m=\u001B[39mtrna_matrix_filepath[i],\n\u001B[1;32m     16\u001B[0m         prep\u001B[38;5;241m=\u001B[39mRNAPrepMethod\u001B[38;5;241m.\u001B[39mTOTAL,\n\u001B[1;32m     17\u001B[0m         taxon\u001B[38;5;241m=\u001B[39mtaxon_id,\n\u001B[1;32m     18\u001B[0m         input_metadata_filepath\u001B[38;5;241m=\u001B[39mPath(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m./data/config_sheets/trna_config.xlsx\u001B[39m\u001B[38;5;124m\"\u001B[39m),\n\u001B[1;32m     19\u001B[0m         replicate_ratio\u001B[38;5;241m=\u001B[39mreplicate_ratio,\n\u001B[1;32m     20\u001B[0m         high_replicate_ratio\u001B[38;5;241m=\u001B[39mhigh_confidence_replicate_ratio,\n\u001B[1;32m     21\u001B[0m         batch_ratio\u001B[38;5;241m=\u001B[39mbatch_ratio,\n\u001B[1;32m     22\u001B[0m         high_batch_ratio\u001B[38;5;241m=\u001B[39mhigh_confidence_batch_ratio,\n\u001B[1;32m     23\u001B[0m         technique\u001B[38;5;241m=\u001B[39mtechnique,\n\u001B[1;32m     24\u001B[0m         cutoff\u001B[38;5;241m=\u001B[39mcutoff\n\u001B[1;32m     25\u001B[0m     )\n",
+      "File \u001B[0;32m~/Projects/COMO/main/como/rnaseq_gen.py:808\u001B[0m, in \u001B[0;36mrnaseq_gen\u001B[0;34m(context_name, input_rnaseq_filepath, input_gene_info_filepath, output_rnaseq_filepath, prep, taxon, input_metadata_filepath, input_metadata_df, replicate_ratio, high_replicate_ratio, batch_ratio, high_batch_ratio, technique, cutoff)\u001B[0m\n\u001B[1;32m    805\u001B[0m logger\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mStarting \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext_name\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m    806\u001B[0m output_rnaseq_filepath\u001B[38;5;241m.\u001B[39mparent\u001B[38;5;241m.\u001B[39mmkdir(parents\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, exist_ok\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[0;32m--> 808\u001B[0m \u001B[38;5;28;01mawait\u001B[39;00m _save_rnaseq_tests(\n\u001B[1;32m    809\u001B[0m     context_name\u001B[38;5;241m=\u001B[39mcontext_name,\n\u001B[1;32m    810\u001B[0m     rnaseq_matrix\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mawait\u001B[39;00m _read_counts(input_rnaseq_filepath),\n\u001B[1;32m    811\u001B[0m     metadata_df\u001B[38;5;241m=\u001B[39minput_metadata_df \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28;01mawait\u001B[39;00m _create_metadata_df(input_metadata_filepath),\n\u001B[1;32m    812\u001B[0m     gene_info_df\u001B[38;5;241m=\u001B[39mpd\u001B[38;5;241m.\u001B[39mread_csv(input_gene_info_filepath),\n\u001B[1;32m    813\u001B[0m     output_filepath\u001B[38;5;241m=\u001B[39moutput_rnaseq_filepath,\n\u001B[1;32m    814\u001B[0m     prep\u001B[38;5;241m=\u001B[39mprep,\n\u001B[1;32m    815\u001B[0m     taxon\u001B[38;5;241m=\u001B[39mtaxon,\n\u001B[1;32m    816\u001B[0m     replicate_ratio\u001B[38;5;241m=\u001B[39mreplicate_ratio,\n\u001B[1;32m    817\u001B[0m     batch_ratio\u001B[38;5;241m=\u001B[39mbatch_ratio,\n\u001B[1;32m    818\u001B[0m     high_replicate_ratio\u001B[38;5;241m=\u001B[39mhigh_replicate_ratio,\n\u001B[1;32m    819\u001B[0m     high_batch_ratio\u001B[38;5;241m=\u001B[39mhigh_batch_ratio,\n\u001B[1;32m    820\u001B[0m     technique\u001B[38;5;241m=\u001B[39mtechnique,\n\u001B[1;32m    821\u001B[0m     cut_off\u001B[38;5;241m=\u001B[39mcutoff,\n\u001B[1;32m    822\u001B[0m )\n",
+      "File \u001B[0;32m~/Projects/COMO/main/como/rnaseq_gen.py:671\u001B[0m, in \u001B[0;36m_save_rnaseq_tests\u001B[0;34m(context_name, rnaseq_matrix, metadata_df, gene_info_df, output_filepath, prep, taxon, replicate_ratio, batch_ratio, high_replicate_ratio, high_batch_ratio, technique, cut_off)\u001B[0m\n\u001B[1;32m    662\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Save the results of the RNA-Seq tests to a CSV file.\"\"\"\u001B[39;00m\n\u001B[1;32m    663\u001B[0m filtering_options \u001B[38;5;241m=\u001B[39m _FilteringOptions(\n\u001B[1;32m    664\u001B[0m     replicate_ratio\u001B[38;5;241m=\u001B[39mreplicate_ratio,\n\u001B[1;32m    665\u001B[0m     batch_ratio\u001B[38;5;241m=\u001B[39mbatch_ratio,\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    668\u001B[0m     high_batch_ratio\u001B[38;5;241m=\u001B[39mhigh_batch_ratio,\n\u001B[1;32m    669\u001B[0m )\n\u001B[0;32m--> 671\u001B[0m read_counts_results: _ReadMatrixResults \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m _build_matrix_results(\n\u001B[1;32m    672\u001B[0m     matrix\u001B[38;5;241m=\u001B[39mrnaseq_matrix,\n\u001B[1;32m    673\u001B[0m     gene_info\u001B[38;5;241m=\u001B[39mgene_info_df,\n\u001B[1;32m    674\u001B[0m     metadata_df\u001B[38;5;241m=\u001B[39mmetadata_df,\n\u001B[1;32m    675\u001B[0m     taxon\u001B[38;5;241m=\u001B[39mtaxon,\n\u001B[1;32m    676\u001B[0m )\n\u001B[1;32m    677\u001B[0m metrics \u001B[38;5;241m=\u001B[39m read_counts_results\u001B[38;5;241m.\u001B[39mmetrics\n\u001B[1;32m    678\u001B[0m entrez_gene_ids \u001B[38;5;241m=\u001B[39m read_counts_results\u001B[38;5;241m.\u001B[39mentrez_gene_ids\n",
+      "File \u001B[0;32m~/Projects/COMO/main/como/rnaseq_gen.py:201\u001B[0m, in \u001B[0;36m_build_matrix_results\u001B[0;34m(matrix, gene_info, metadata_df, taxon)\u001B[0m\n\u001B[1;32m    199\u001B[0m gene_info \u001B[38;5;241m=\u001B[39m gene_info_migrations(gene_info)\n\u001B[1;32m    200\u001B[0m \u001B[38;5;28mprint\u001B[39m(matrix)\n\u001B[0;32m--> 201\u001B[0m conversion \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m ensembl_to_gene_id_and_symbol(ids\u001B[38;5;241m=\u001B[39m\u001B[43mmatrix\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mensembl_gene_id\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241m.\u001B[39mtolist(), taxon\u001B[38;5;241m=\u001B[39mtaxon)\n\u001B[1;32m    202\u001B[0m matrix \u001B[38;5;241m=\u001B[39m matrix\u001B[38;5;241m.\u001B[39mmerge(conversion, on\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mensembl_gene_id\u001B[39m\u001B[38;5;124m\"\u001B[39m, how\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mleft\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m    204\u001B[0m \u001B[38;5;66;03m# Only include Entrez and Ensembl Gene IDs that are present in `gene_info`\u001B[39;00m\n",
+      "File \u001B[0;32m~/Projects/COMO/.venv/lib/python3.10/site-packages/pandas/core/frame.py:4102\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[0;34m(self, key)\u001B[0m\n\u001B[1;32m   4100\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcolumns\u001B[38;5;241m.\u001B[39mnlevels \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[1;32m   4101\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_getitem_multilevel(key)\n\u001B[0;32m-> 4102\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolumns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   4103\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_integer(indexer):\n\u001B[1;32m   4104\u001B[0m     indexer \u001B[38;5;241m=\u001B[39m [indexer]\n",
+      "File \u001B[0;32m~/Projects/COMO/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[0;34m(self, key)\u001B[0m\n\u001B[1;32m   3807\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(casted_key, \u001B[38;5;28mslice\u001B[39m) \u001B[38;5;129;01mor\u001B[39;00m (\n\u001B[1;32m   3808\u001B[0m         \u001B[38;5;28misinstance\u001B[39m(casted_key, abc\u001B[38;5;241m.\u001B[39mIterable)\n\u001B[1;32m   3809\u001B[0m         \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28many\u001B[39m(\u001B[38;5;28misinstance\u001B[39m(x, \u001B[38;5;28mslice\u001B[39m) \u001B[38;5;28;01mfor\u001B[39;00m x \u001B[38;5;129;01min\u001B[39;00m casted_key)\n\u001B[1;32m   3810\u001B[0m     ):\n\u001B[1;32m   3811\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m InvalidIndexError(key)\n\u001B[0;32m-> 3812\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[1;32m   3813\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[1;32m   3814\u001B[0m     \u001B[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001B[39;00m\n\u001B[1;32m   3815\u001B[0m     \u001B[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001B[39;00m\n\u001B[1;32m   3816\u001B[0m     \u001B[38;5;66;03m#  the TypeError.\u001B[39;00m\n\u001B[1;32m   3817\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_indexing_error(key)\n",
+      "\u001B[0;31mKeyError\u001B[0m: 'ensembl_gene_id'"
      ]
     }
    ],

From 3825aa259e4d3c71a648700c31dfa32962b5f45e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 10 Dec 2024 15:48:06 -0600
Subject: [PATCH 074/345] refactor: move filtering technique to types

---
 main/como/rnaseq_gen.py | 27 +--------------------------
 main/como/types.py      | 25 +++++++++++++++++++++++++
 2 files changed, 26 insertions(+), 26 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 05f185f3..5d2e364e 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -27,7 +27,7 @@
 
 from como.migrations import gene_info_migrations
 from como.project import Config
-from como.types import RNAPrepMethod
+from como.types import FilteringTechnique, RNAPrepMethod
 
 
 class _FilteringOptions(NamedTuple):
@@ -38,31 +38,6 @@ class _FilteringOptions(NamedTuple):
     high_batch_ratio: float
 
 
-class FilteringTechnique(Enum):
-    """RNA sequencing filtering capabilities."""
-
-    cpm = "cpm"
-    zfpkm = "zfpkm"
-    tpm = "quantile"
-    umi = "umi"
-
-    @staticmethod
-    def from_string(value: str) -> FilteringTechnique:
-        """Create a filtering technique object from a string."""
-        match value.lower():
-            case "cpm":
-                return FilteringTechnique.cpm
-            case "zfpkm":
-                return FilteringTechnique.zfpkm
-            case "quantile":
-                return FilteringTechnique.tpm
-            case "umi":
-                return FilteringTechnique.umi
-            case _:
-                possible_values = [t.value for t in FilteringTechnique]
-                raise ValueError(f"Got a filtering technique of '{value}'; should be one of: {possible_values}")
-
-
 class LayoutMethod(Enum):
     """RNA sequencing layout method."""
 
diff --git a/main/como/types.py b/main/como/types.py
index 42908252..916d3a25 100644
--- a/main/como/types.py
+++ b/main/como/types.py
@@ -46,5 +46,30 @@ def from_string(value: str) -> RNAPrepMethod:
                 raise ValueError(f"Filtering technique must be one of {possible_values}; got: {value}")
 
 
+class FilteringTechnique(Enum):
+    """RNA sequencing filtering capabilities."""
+
+    cpm = "cpm"
+    zfpkm = "zfpkm"
+    tpm = "quantile"
+    umi = "umi"
+
+    @staticmethod
+    def from_string(value: str) -> FilteringTechnique:
+        """Create a filtering technique object from a string."""
+        match value.lower():
+            case "cpm":
+                return FilteringTechnique.cpm
+            case "zfpkm":
+                return FilteringTechnique.zfpkm
+            case "quantile":
+                return FilteringTechnique.tpm
+            case "umi":
+                return FilteringTechnique.umi
+            case _:
+                possible_values = [t.value for t in FilteringTechnique]
+                raise ValueError(f"Got a filtering technique of '{value}'; should be one of: {possible_values}")
+
+
 type_path = str | Path
 type_rna = Literal["total", "mrna"]

From b0fa60eb3d80f1ebe994dad4c26043099aefaa59 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 10 Dec 2024 15:48:17 -0600
Subject: [PATCH 075/345] fix: do not use more cores than necessary

---
 main/como/rnaseq_gen.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 5d2e364e..8274a0d3 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -356,7 +356,7 @@ def zfpkm_transform(
 
     total = len(fpkm_df.columns)
     update_per_step: int = int(np.ceil(total * update_every_percent))
-    cores = multiprocessing.cpu_count() - 2
+    cores = min(multiprocessing.cpu_count() - 2, total)
     logger.debug(f"Processing {total:,} samples through zFPKM transform using {cores} cores")
     logger.debug(
         f"Will update every {update_per_step:,} steps as this is approximately "

From 9539711edb02e0fc25ba355ffd16326867daf253 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Wed, 11 Dec 2024 14:22:29 -0600
Subject: [PATCH 076/345] chore: merge dependabot updates into hotfix

---
 .github/dependabot.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/dependabot.yml b/.github/dependabot.yml
index 438cb6fa..5ea2decd 100644
--- a/.github/dependabot.yml
+++ b/.github/dependabot.yml
@@ -2,6 +2,7 @@ version: 2
 updates:
   - package-ecosystem: "github-actions"
     directory: "/"
+    target-branch: "hotfix"
     schedule:
       # Check for updates to GitHub Actions every weekday
       interval: "daily"

From 6bd06e1875e8c37747bb8adf4eddfbb45608c240 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Wed, 11 Dec 2024 14:37:58 -0600
Subject: [PATCH 077/345] fix: use parenthesis to validate calculations

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 8274a0d3..364bd4c3 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -505,7 +505,7 @@ def cpm_filter(
             cutoff = (
                 10e6 / (np.median(np.sum(counts[:, i])))
                 if cut_off == "default"
-                else 1e6 * cut_off / np.median(np.sum(counts[:, i]))
+                else (1e6 * cut_off) / np.median(np.sum(counts[:, i]))
             )
             test_bools = test_bools.merge(counts_per_million[counts_per_million.iloc[:, i] > cutoff])
 
@@ -637,7 +637,7 @@ async def _save_rnaseq_tests(
     filtering_options = _FilteringOptions(
         replicate_ratio=replicate_ratio,
         batch_ratio=batch_ratio,
-        cut_off=cut_off,
+        cut_off=float(cut_off),
         high_replicate_ratio=high_replicate_ratio,
         high_batch_ratio=high_batch_ratio,
     )
@@ -725,9 +725,14 @@ async def rnaseq_gen(  # noqa: C901, allow complex function
         then study/batch numbers are checked for consensus according to batch ratios.
     The zFPKM method is outlined here: https://pubmed.ncbi.nlm.nih.gov/24215113/
 
-    :param metadata_filepath: The configuration filename to read
+    :param context_name: The name of the context being processed
+    :param input_rnaseq_filepath: The filepath to the gene count matrix
+    :param input_gene_info_filepath: The filepath to the gene info file
+    :param output_rnaseq_filepath: The filepath to write the output gene count matrix
     :param prep: The preparation method
     :param taxon: The NCBI Taxon ID
+    :param input_metadata_filepath: The filepath to the metadata file
+    :param input_metadata_df: The metadata dataframe
     :param replicate_ratio: The percentage of replicates that a gene must
         appear in for a gene to be marked as "active" in a batch/study
     :param batch_ratio: The percentage of batches that a gene must appear in for a gene to be marked as 'active"
@@ -759,7 +764,7 @@ async def rnaseq_gen(  # noqa: C901, allow complex function
                 cutoff = "default"
 
         case FilteringTechnique.zfpkm:
-            cutoff = "default" if cutoff else cutoff
+            cutoff = cutoff or -3
         case FilteringTechnique.umi:
             pass
         case _:

From f24347d14e00f6817114ef049ab6b5a09cd659b4 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Wed, 11 Dec 2024 14:41:54 -0600
Subject: [PATCH 078/345] refactor: ignore missing variables for now

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/COMO.ipynb | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/main/COMO.ipynb b/main/COMO.ipynb
index 52d7fb88..bbaeb152 100644
--- a/main/COMO.ipynb
+++ b/main/COMO.ipynb
@@ -442,7 +442,7 @@
     }
    ],
    "source": [
-    "from como.rnaseq_gen import rnaseq_gen, FilteringTechnique\n",
+    "from como.rnaseq_gen import FilteringTechnique, rnaseq_gen\n",
     "\n",
     "replicate_ratio = 0.75\n",
     "high_confidence_replicate_ratio = 1.0\n",
@@ -452,7 +452,7 @@
     "cutoff = -3\n",
     "\n",
     "for i, context in enumerate(context_names):\n",
-    "    await rnaseq_gen(  # noqa\n",
+    "    await rnaseq_gen(\n",
     "        context_name=context,\n",
     "        input_rnaseq_filepath=trna_matrix_filepath[i],\n",
     "        input_gene_info_filepath=gene_info_filepath[i],\n",
@@ -494,7 +494,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from como.rnaseq_gen import rnaseq_gen, FilteringTechnique\n",
+    "from como.rnaseq_gen import FilteringTechnique, rnaseq_gen\n",
     "\n",
     "replicate_ratio = 0.75\n",
     "high_confidence_replicate_ratio = 1.0\n",
@@ -546,7 +546,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from como.rnaseq_gen import rnaseq_gen, FilteringTechnique\n",
+    "from como.rnaseq_gen import FilteringTechnique, rnaseq_gen\n",
     "\n",
     "replicate_ratio = 0.75\n",
     "high_confidence_replicate_ratio = 1.0\n",
@@ -744,8 +744,8 @@
     "    [\n",
     "        \"python3\", \"como/merge_xomics.py\",\n",
     "        \"--merge-zfpkm-distribution\",\n",
-    "        \"--total-rnaseq-config-file\", trnaseq_config_file,\n",
-    "        \"--mrnaseq-config-file\", mrnaseq_config_file,\n",
+    "        \"--total-rnaseq-config-file\", trnaseq_config_file,  # noqa: F821\n",
+    "        \"--mrnaseq-config-file\", mrnaseq_config_file,  # noqa: F821\n",
     "        # \"--scrnaseq-config-file\", scrnaseq_config_file,  # If using single-cell data, uncomment the start of this line\n",
     "        # \"--proteomics-config-file\", proteomics_config_file,  # If using proteomics data, uncomment the start of this line\n",
     "        \"--requirement-adjust\", requirement_adjust,\n",

From 1956721275442f45eae829d592671810647f0832 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Wed, 11 Dec 2024 14:43:14 -0600
Subject: [PATCH 079/345] chore: ignore complex function

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 5c2d8125..02702b38 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -278,7 +278,7 @@ async def _write_counts_matrix(
     return final_matrix
 
 
-async def _create_config_df(
+async def _create_config_df(  # noqa: C901
     context_name: str,
     /,
     como_context_dir: Path,

From 65bd2a3a43cf868d4abf0d505d70bf6bf5a7ef17 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Wed, 11 Dec 2024 15:12:21 -0600
Subject: [PATCH 080/345] chore: remove unused imports

---
 main/como/proteomics_gen.py | 33 +++++++++++++++++++++++----------
 1 file changed, 23 insertions(+), 10 deletions(-)

diff --git a/main/como/proteomics_gen.py b/main/como/proteomics_gen.py
index b1e17b34..1b62bac2 100644
--- a/main/como/proteomics_gen.py
+++ b/main/como/proteomics_gen.py
@@ -1,5 +1,6 @@
-import argparse
-import asyncio
+from __future__ import annotations
+
+from pathlib import Path
 
 import numpy as np
 import pandas as pd
@@ -12,7 +13,7 @@
 
 
 # Load Proteomics
-def load_proteomics_data(datafilename, context_name):
+def process_proteomics_data(path: Path) -> pd.DataFrame:
     """Load proteomics data from a given context and filename."""
     config = Config()
     data_path = config.data_dir / "data_matrices" / context_name / datafilename
@@ -45,7 +46,7 @@ def load_proteomics_data(datafilename, context_name):
 
 
 # read map to convert to entrez
-async def load_gene_symbol_map(gene_symbols: list[str]):
+async def load_gene_symbol_map(gene_symbols: list[str], entrez_map: Path | None = None):
     """Add descirption...."""
     config = Config()
     filepath = config.data_dir / "proteomics_entrez_map.csv"
@@ -62,7 +63,15 @@ async def load_gene_symbol_map(gene_symbols: list[str]):
     return df[~df.index.duplicated()]
 
 
-def abundance_to_bool_group(context_name, group_name, abundance_matrix, rep_ratio, hi_rep_ratio, quantile):
+def abundance_to_bool_group(
+    context_name,
+    group_name,
+    abundance_matrix,
+    replicate_ratio,
+    high_confidence_replicate_ratio,
+    quantile,
+    output_boolean_filepath: Path,
+):
     """Descrioption...."""
     config = Config()
     output_dir = config.result_dir / context_name / "proteomics"
@@ -156,11 +165,15 @@ def load_empty_dict():
 
 
 async def proteomics_gen(
-    config_file: str,
-    rep_ratio: float = 0.5,
-    group_ratio: float = 0.5,
-    hi_rep_ratio: float = 0.5,
-    hi_group_ratio: float = 0.5,
+    context_name: str,
+    config_filepath: Path,
+    matrix_filepath: Path,
+    output_boolean_filepath: Path,
+    input_entrez_map: Path | None = None,
+    replicate_ratio: float = 0.5,
+    batch_ratio: float = 0.5,
+    high_confidence_replicate_ratio: float = 0.7,
+    high_confience_batch_ratio: float = 0.7,
     quantile: int = 25,
 ):
     """Generate proteomics data."""

From 3cc9bec93f893af7f35786ecb4f4d1ad58795744 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Wed, 11 Dec 2024 15:14:13 -0600
Subject: [PATCH 081/345] refactor: remove command line interface

---
 main/como/proteomics_gen.py | 75 -------------------------------------
 1 file changed, 75 deletions(-)

diff --git a/main/como/proteomics_gen.py b/main/como/proteomics_gen.py
index 1b62bac2..366f7bf5 100644
--- a/main/como/proteomics_gen.py
+++ b/main/como/proteomics_gen.py
@@ -218,80 +218,5 @@ async def proteomics_gen(
         to_bool_context(context_name, group_ratio, hi_group_ratio, groups)
 
 
-def _main():
-    parser = argparse.ArgumentParser(
-        prog="proteomics_gen.py",
-        description="Description goes here",
-        epilog="For additional help, please post questions/issues in the MADRID GitHub repo at "
-        "https://github.com/HelikarLab/MADRID or email babessell@gmail.com",
-    )
-    parser.add_argument(
-        "-c",
-        "--config-file",
-        type=str,
-        required=True,
-        dest="config_file",
-        help="The configuration file for proteomics",
-    )
-    parser.add_argument(
-        "-r",
-        "--replicate-ratio",
-        type=float,
-        required=False,
-        default=0.5,
-        dest="rep_ratio",
-        help="Ratio of replicates required for a gene to be considered active in that group",
-    )
-    parser.add_argument(
-        "-b",
-        "--batch-ratio",
-        type=float,
-        required=False,
-        default=0.5,
-        dest="group_ratio",
-        help="Ratio of groups (batches or studies) required for a gene to be considered active in a context",
-    )
-    parser.add_argument(
-        "-hr",
-        "--high-replicate-ratio",
-        type=float,
-        required=False,
-        default=0.5,
-        dest="hi_rep_ratio",
-        help="Ratio of replicates required for a gene to be considered high-confidence in that group",
-    )
-    parser.add_argument(
-        "-hb",
-        "--high-batch-ratio",
-        type=float,
-        required=False,
-        default=0.5,
-        dest="hi_group_ratio",
-        help="Ratio of groups (batches or studies) required for a gene to be considered high-confidence in a context",
-    )
-
-    parser.add_argument(
-        "-q",
-        "--quantile",
-        type=int,
-        required=False,
-        default=25,
-        dest="quantile",
-        help="The quantile of genes to accept. This should be an integer from 0% (no proteins pass) "
-        "to 100% (all proteins pass).",
-    )
-    args = parser.parse_args()
-    asyncio.run(
-        proteomics_gen(
-            args.config_file,
-            args.rep_ratio,
-            args.group_ratio,
-            args.hi_rep_ratio,
-            args.hi_group_ratio,
-            args.quantile,
         )
     )
-
-
-if __name__ == "__main__":
-    _main()

From 0f9bd3a35ab27b6aeed9f7d4735c441b732055fb Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Wed, 11 Dec 2024 15:14:44 -0600
Subject: [PATCH 082/345] feat: process input data more pythonically

---
 main/como/proteomics_gen.py | 35 ++++++++---------------------------
 1 file changed, 8 insertions(+), 27 deletions(-)

diff --git a/main/como/proteomics_gen.py b/main/como/proteomics_gen.py
index 366f7bf5..d71ded3f 100644
--- a/main/como/proteomics_gen.py
+++ b/main/como/proteomics_gen.py
@@ -15,34 +15,15 @@
 # Load Proteomics
 def process_proteomics_data(path: Path) -> pd.DataFrame:
     """Load proteomics data from a given context and filename."""
-    config = Config()
-    data_path = config.data_dir / "data_matrices" / context_name / datafilename
-    logger.info(f"Data Matrix Path: {data_path}")
-
-    if data_path.exists():
-        proteomics_data = pd.read_csv(data_path, header=0)
-    else:
-        logger.error(f"Error: file not found: {data_path}")
-
-        return None
-
     # Preprocess data, drop na, duplicate ';' in symbol,
-    proteomics_data["gene_symbol"] = proteomics_data["gene_symbol"].astype(str)
-    proteomics_data.dropna(subset=["gene_symbol"], inplace=True)
-    pluralnames = proteomics_data[proteomics_data["gene_symbol"].str.contains(";") == True]  # noqa: E712
-
-    for idx, row in pluralnames.iterrows():
-        names = row["gene_symbol"].split(";")
-        rows = []
-
-        for name in names:
-            rowcopy = row.copy()
-            rowcopy["gene_symbol"] = name
-            rows.append(rowcopy)
-        proteomics_data.drop(index=idx, inplace=True)
-        proteomics_data = pd.concat([proteomics_data, pd.DataFrame(rows)], ignore_index=True)
-
-    return proteomics_data
+    matrix: pd.DataFrame = pd.read_csv(path)
+    if "gene_symbol" not in matrix.columns:
+        raise ValueError("No gene_symbol column found in proteomics data.")
+
+    matrix["gene_symbol"] = matrix["gene_symbol"].astype(str)
+    matrix.dropna(subset=["gene_symbol"], inplace=True)
+    matrix = matrix.assign(gene_symbol=matrix["gene_symbol"].str.split(";")).explode("gene_symbol")
+    return matrix
 
 
 # read map to convert to entrez

From 1402016337ac0d2a1ad8dc74db21615d40e4947f Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Wed, 11 Dec 2024 15:15:39 -0600
Subject: [PATCH 083/345] refactor: do not use hardcoded filepaths

---
 main/como/proteomics_gen.py | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/main/como/proteomics_gen.py b/main/como/proteomics_gen.py
index d71ded3f..8f91f41a 100644
--- a/main/como/proteomics_gen.py
+++ b/main/como/proteomics_gen.py
@@ -29,10 +29,8 @@ def process_proteomics_data(path: Path) -> pd.DataFrame:
 # read map to convert to entrez
 async def load_gene_symbol_map(gene_symbols: list[str], entrez_map: Path | None = None):
     """Add descirption...."""
-    config = Config()
-    filepath = config.data_dir / "proteomics_entrez_map.csv"
-    if filepath.exists():
-        df = pd.read_csv(filepath, index_col="gene_symbol")
+    if entrez_map and entrez_map.exists():
+        df = pd.read_csv(entrez_map, index_col="gene_symbol")
     else:
         biodbnet = BioDBNet()
         df = await biodbnet.async_db2db(
@@ -158,15 +156,20 @@ async def proteomics_gen(
     quantile: int = 25,
 ):
     """Generate proteomics data."""
-    config = Config()
-    if not config_file:
-        raise ValueError("Config file must be provided")
+    if not config_filepath.exists():
+        raise FileNotFoundError(f"Config file not found at {config_filepath}")
+    if config_filepath.suffix not in (".xlsx", ".xls"):
+        raise FileNotFoundError(f"Config file must be an xlsx or xls file at {config_filepath}")
+
+    if not matrix_filepath.exists():
+        raise FileNotFoundError(f"Matrix file not found at {matrix_filepath}")
+    if matrix_filepath.suffix not in {".csv"}:
+        raise FileNotFoundError(f"Matrix file must be a csv file at {matrix_filepath}")
 
     if quantile < 0 or quantile > 100:
         raise ValueError("Quantile must be an integer from 0 to 100")
     quantile /= 100
 
-    prot_config_filepath = config.data_dir / "config_sheets" / config_file
     logger.info(f"Config file is at '{prot_config_filepath}'")
 
     xl = pd.ExcelFile(prot_config_filepath)

From 450c9e954e2d195b549bfe0706592c86ca010111 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Wed, 11 Dec 2024 15:16:01 -0600
Subject: [PATCH 084/345] chore: ruff formatting

---
 main/como/proteomics_gen.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/main/como/proteomics_gen.py b/main/como/proteomics_gen.py
index 8f91f41a..4f2b7475 100644
--- a/main/como/proteomics_gen.py
+++ b/main/como/proteomics_gen.py
@@ -34,7 +34,9 @@ async def load_gene_symbol_map(gene_symbols: list[str], entrez_map: Path | None
     else:
         biodbnet = BioDBNet()
         df = await biodbnet.async_db2db(
-            values=gene_symbols, input_db=Input.GENE_SYMBOL, output_db=[Output.GENE_ID, Output.ENSEMBL_GENE_ID]
+            values=gene_symbols,
+            input_db=Input.GENE_SYMBOL,
+            output_db=[Output.GENE_ID, Output.ENSEMBL_GENE_ID],
         )
         df.loc[df["gene_id"] == "-", ["gene_id"]] = np.nan
         df.to_csv(filepath, index_label="gene_symbol")

From 01b31dfe1ad1e0bfba3ac5b66b99fb85f36694a6 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Wed, 11 Dec 2024 15:16:31 -0600
Subject: [PATCH 085/345] style: use better variable name & reorganize

---
 main/como/proteomics_gen.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/main/como/proteomics_gen.py b/main/como/proteomics_gen.py
index 4f2b7475..e649cd8b 100644
--- a/main/como/proteomics_gen.py
+++ b/main/como/proteomics_gen.py
@@ -39,7 +39,7 @@ async def load_gene_symbol_map(gene_symbols: list[str], entrez_map: Path | None
             output_db=[Output.GENE_ID, Output.ENSEMBL_GENE_ID],
         )
         df.loc[df["gene_id"] == "-", ["gene_id"]] = np.nan
-        df.to_csv(filepath, index_label="gene_symbol")
+        df.to_csv(entrez_map, index_label="gene_symbol")
 
     return df[~df.index.duplicated()]
 
@@ -75,12 +75,11 @@ def abundance_to_bool_group(
 
     abundance_matrix["pos"] = (abundance_matrix > 0).sum(axis=1) / abundance_matrix.count(axis=1)
     abundance_matrix["expressed"] = 0
-    abundance_matrix.loc[(abundance_matrix["pos"] >= rep_ratio), ["expressed"]] = 1
     abundance_matrix["high"] = 0
-    abundance_matrix.loc[(abundance_matrix["pos"] >= hi_rep_ratio), ["high"]] = 1
+    abundance_matrix.loc[(abundance_matrix["pos"] >= replicate_ratio), ["expressed"]] = 1
+    abundance_matrix.loc[(abundance_matrix["pos"] >= high_confidence_replicate_ratio), ["high"]] = 1
 
-    bool_filepath = output_dir / f"bool_prot_Matrix_{context_name}_{group_name}.csv"
-    abundance_matrix.to_csv(bool_filepath, index_label="entrez_gene_id")
+    abundance_matrix.to_csv(output_boolean_filepath, index_label="entrez_gene_id")
 
 
 def to_bool_context(context_name, group_ratio, hi_group_ratio, group_names):

From 06e0932f398db82d429b522663d648ce67ae25df Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Wed, 11 Dec 2024 15:16:45 -0600
Subject: [PATCH 086/345] feat: process the provided file paths

---
 main/como/proteomics_gen.py | 63 ++++++++++++++++++++-----------------
 1 file changed, 34 insertions(+), 29 deletions(-)

diff --git a/main/como/proteomics_gen.py b/main/como/proteomics_gen.py
index e649cd8b..50f1cfbb 100644
--- a/main/como/proteomics_gen.py
+++ b/main/como/proteomics_gen.py
@@ -171,37 +171,42 @@ async def proteomics_gen(
         raise ValueError("Quantile must be an integer from 0 to 100")
     quantile /= 100
 
-    logger.info(f"Config file is at '{prot_config_filepath}'")
+    config_df = pd.read_excel(config_filepath, sheet_name=context_name)
+    matrix: pd.DataFrame = process_proteomics_data(matrix_filepath)
 
-    xl = pd.ExcelFile(prot_config_filepath)
-    sheet_names = xl.sheet_names
-
-    for context_name in sheet_names:
-        datafilename = "".join(["protein_abundance_", context_name, ".csv"])
-        config_sheet = pd.read_excel(prot_config_filepath, sheet_name=context_name)
-        groups = config_sheet["group"].unique().tolist()
-
-        for group in groups:
-            group_idx = np.where([g == group for g in config_sheet["group"].tolist()])
-            cols = [*np.take(config_sheet["sample_name"].to_numpy(), group_idx).ravel().tolist(), "gene_symbol"]
-
-            proteomics_data = load_proteomics_data(datafilename, context_name)
-            proteomics_data = proteomics_data.loc[:, cols]
-
-            symbols_to_ids = await load_gene_symbol_map(gene_symbols=proteomics_data["gene_symbol"].tolist())
-            proteomics_data.dropna(subset=["gene_symbol"], inplace=True)
-            if "uniprot" in proteomics_data.columns:
-                proteomics_data.drop(columns=["uniprot"], inplace=True)
-
-            proteomics_data = proteomics_data.groupby(["gene_symbol"]).agg("max")
-            proteomics_data["entrez_gene_id"] = symbols_to_ids["gene_id"]
-            proteomics_data.dropna(subset=["entrez_gene_id"], inplace=True)
-            proteomics_data.set_index("entrez_gene_id", inplace=True)
-
-            # save proteomics data by test
-            abundance_to_bool_group(context_name, group, proteomics_data, rep_ratio, hi_rep_ratio, quantile)
-        to_bool_context(context_name, group_ratio, hi_group_ratio, groups)
+    groups = config_df["group"].unique().tolist()
 
+    for group in groups:
+        indices = np.where([g == group for g in config_df["group"]])
+        sample_columns = [*np.take(config_df["sample_name"].to_numpy(), indices).ravel().tolist(), "gene_symbol"]
+        matrix = matrix.loc[:, sample_columns]
 
+        symbols_to_gene_ids = await load_gene_symbol_map(
+            gene_symbols=matrix["gene_symbol"].tolist(),
+            entrez_map=input_entrez_map,
+        )
+        matrix.dropna(subset=["gene_symbol"], inplace=True)
+        if "uniprot" in matrix.columns:
+            matrix.drop(columns=["uniprot"], inplace=True)
+
+        matrix = matrix.groupby(["gene_symbol"]).agg("max")
+        matrix["entrez_gene_id"] = symbols_to_gene_ids["gene_id"]
+        matrix.dropna(subset=["entrez_gene_id"], inplace=True)
+        matrix.set_index("entrez_gene_id", inplace=True)
+
+        # bool_filepath = output_dir / f"bool_prot_Matrix_{context_name}_{group_name}.csv"
+        abundance_to_bool_group(
+            context_name=context_name,
+            group_name=group,
+            abundance_matrix=matrix,
+            replicate_ratio=replicate_ratio,
+            high_confidence_replicate_ratio=high_confidence_replicate_ratio,
+            quantile=quantile,
+            output_boolean_filepath=output_boolean_filepath,
         )
+    to_bool_context(
+        context_name=context_name,
+        group_ratio=batch_ratio,
+        hi_group_ratio=high_confience_batch_ratio,
+        group_names=groups,
     )

From 4baf95a393e925d4f865b2d7f3f8a9608459ce4c Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Wed, 11 Dec 2024 15:17:19 -0600
Subject: [PATCH 087/345] feat: write to provided filepath

---
 main/como/proteomics_preprocessing.py | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/main/como/proteomics_preprocessing.py b/main/como/proteomics_preprocessing.py
index 9b215439..2fee5290 100644
--- a/main/como/proteomics_preprocessing.py
+++ b/main/como/proteomics_preprocessing.py
@@ -129,12 +129,12 @@ def plot_gaussian_fit(z_results: ZResult, facet_titles: bool = True, x_min: int
 
 
 # Main function for protein abundance transformation
-def protein_transform_main(abundance_df: pd.DataFrame | str | Path, out_dir: str | Path, group_name: str) -> None:
+def protein_transform_main(
+    abundance_df: pd.DataFrame | str | Path,
+    output_gaussian_img_filepath: Path,
+    output_z_score_matrix_filepath: Path,
+) -> None:
     """Transform protein abundance data."""
-    out_dir: Path = Path(out_dir)
-    output_figure_directory = out_dir / "figures"
-    output_figure_directory.mkdir(parents=True, exist_ok=True)
-
     abundance_df: pd.DataFrame = (
         pd.read_csv(abundance_df) if isinstance(abundance_df, (str, Path)) else abundance_df.fillna(0)
     )
@@ -142,10 +142,10 @@ def protein_transform_main(abundance_df: pd.DataFrame | str | Path, out_dir: str
     z_transform: ZResult = z_score_calc(abundance_df, min_thresh=0)
 
     fig = plot_gaussian_fit(z_results=z_transform, facet_titles=True, x_min=-4)
-    fig.write_image(out_dir / "gaussian_fit.png")
-    fig.write_html(out_dir / "gaussian_fit.html")
-    logger.info(f"Wrote image to {out_dir / 'gaussian_fit.png'}")
+    fig.write_image(output_gaussian_img_filepath.with_suffix(".png"))
+    fig.write_html(output_gaussian_img_filepath.with_suffix(".html"))
+    logger.info(f"Gaussian fit figure written to {output_gaussian_img_filepath}")
 
     z_transformed_abundances = z_transform.zfpkm
     z_transformed_abundances[abundance_df == 0] = -4
-    z_transformed_abundances.to_csv(out_dir / f"protein_zscore_Matrix_{group_name}.csv", index=False)
+    z_transformed_abundances.to_csv(output_z_score_matrix_filepath, index=False)

From 9f9700163ae5b7dcef98faff70435c5114a46ccc Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Wed, 11 Dec 2024 15:26:04 -0600
Subject: [PATCH 088/345] refactor: remove hardcoded filepaths

---
 main/como/proteomics_gen.py | 16 ++++++----------
 1 file changed, 6 insertions(+), 10 deletions(-)

diff --git a/main/como/proteomics_gen.py b/main/como/proteomics_gen.py
index 50f1cfbb..9f216d6d 100644
--- a/main/como/proteomics_gen.py
+++ b/main/como/proteomics_gen.py
@@ -53,17 +53,13 @@ def abundance_to_bool_group(
     quantile,
     output_boolean_filepath: Path,
 ):
-    """Descrioption...."""
-    config = Config()
-    output_dir = config.result_dir / context_name / "proteomics"
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    # write group abundances to individual files
-    abundance_filepath = (
-        config.result_dir / context_name / "proteomics" / "".join(["protein_abundance_", group_name, ".csv"])
-    )
+    """Convert proteomic data to boolean expression."""
     abundance_matrix.to_csv(abundance_filepath, index_label="entrez_gene_id")
-    protein_transform_main(abundance_matrix, output_dir, group_name)
+    protein_transform_main(
+        abundance_df=abundance_matrix,
+        output_gaussian_img_filepath=output_gaussian_img_filepath,
+        output_z_score_matrix_filepath=output_z_score_matrix_filepath,
+    )
 
     # Logical Calculation
     abundance_matrix_nozero = abundance_matrix.replace(0, np.nan)

From bfe8d7e4ec483704765c73599277caa58fc82fa5 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Wed, 11 Dec 2024 15:26:24 -0600
Subject: [PATCH 089/345] refactor: pythonic approach to processing

---
 main/como/proteomics_gen.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/main/como/proteomics_gen.py b/main/como/proteomics_gen.py
index 9f216d6d..2c312553 100644
--- a/main/como/proteomics_gen.py
+++ b/main/como/proteomics_gen.py
@@ -64,14 +64,14 @@ def abundance_to_bool_group(
     # Logical Calculation
     abundance_matrix_nozero = abundance_matrix.replace(0, np.nan)
     thresholds = abundance_matrix_nozero.quantile(quantile, axis=0)
-    testbool = pd.DataFrame(0, columns=list(abundance_matrix), index=abundance_matrix.index)
+    testbool = pd.DataFrame(0, columns=abundance_matrix.columns, index=abundance_matrix.index)
 
-    for col in list(abundance_matrix):
+    for col in abundance_matrix.columns:
         testbool.loc[abundance_matrix[col] > thresholds[col], [col]] = 1
 
-    abundance_matrix["pos"] = (abundance_matrix > 0).sum(axis=1) / abundance_matrix.count(axis=1)
     abundance_matrix["expressed"] = 0
     abundance_matrix["high"] = 0
+    abundance_matrix["pos"] = abundance_matrix[abundance_matrix > 0].sum(axis=1) / abundance_matrix.count(axis=1)
     abundance_matrix.loc[(abundance_matrix["pos"] >= replicate_ratio), ["expressed"]] = 1
     abundance_matrix.loc[(abundance_matrix["pos"] >= high_confidence_replicate_ratio), ["high"]] = 1
 

From c40169611068898ba36b26cad39b51016236b8e8 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Wed, 11 Dec 2024 15:26:37 -0600
Subject: [PATCH 090/345] fix: arguments to write data

---
 main/como/proteomics_gen.py | 18 ++++++++++++------
 1 file changed, 12 insertions(+), 6 deletions(-)

diff --git a/main/como/proteomics_gen.py b/main/como/proteomics_gen.py
index 2c312553..9a53304e 100644
--- a/main/como/proteomics_gen.py
+++ b/main/como/proteomics_gen.py
@@ -46,11 +46,13 @@ async def load_gene_symbol_map(gene_symbols: list[str], entrez_map: Path | None
 
 def abundance_to_bool_group(
     context_name,
-    group_name,
-    abundance_matrix,
-    replicate_ratio,
-    high_confidence_replicate_ratio,
-    quantile,
+    abundance_filepath: Path,
+    output_gaussian_img_filepath: Path,
+    output_z_score_matrix_filepath: Path,
+    abundance_matrix: pd.DataFrame,
+    replicate_ratio: float,
+    high_confidence_replicate_ratio: float,
+    quantile: float,
     output_boolean_filepath: Path,
 ):
     """Convert proteomic data to boolean expression."""
@@ -145,6 +147,8 @@ async def proteomics_gen(
     config_filepath: Path,
     matrix_filepath: Path,
     output_boolean_filepath: Path,
+    output_gaussian_img_filepath: Path,
+    output_z_score_matrix_filepath: Path,
     input_entrez_map: Path | None = None,
     replicate_ratio: float = 0.5,
     batch_ratio: float = 0.5,
@@ -193,12 +197,14 @@ async def proteomics_gen(
         # bool_filepath = output_dir / f"bool_prot_Matrix_{context_name}_{group_name}.csv"
         abundance_to_bool_group(
             context_name=context_name,
-            group_name=group,
+            abundance_filepath=matrix_filepath,
             abundance_matrix=matrix,
             replicate_ratio=replicate_ratio,
             high_confidence_replicate_ratio=high_confidence_replicate_ratio,
             quantile=quantile,
             output_boolean_filepath=output_boolean_filepath,
+            output_gaussian_img_filepath=output_gaussian_img_filepath,
+            output_z_score_matrix_filepath=output_z_score_matrix_filepath,
         )
     to_bool_context(
         context_name=context_name,

From c7da9718068eb054410d49e842332e1b4b847964 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Wed, 11 Dec 2024 16:03:18 -0600
Subject: [PATCH 091/345] feat: allow specifiying files to process

---
 main/COMO.ipynb                       | 222 +++++++++-----------------
 main/como/proteomics_gen.py           |  16 +-
 main/como/proteomics_preprocessing.py |  13 +-
 3 files changed, 96 insertions(+), 155 deletions(-)

diff --git a/main/COMO.ipynb b/main/COMO.ipynb
index bbaeb152..3cbebec1 100644
--- a/main/COMO.ipynb
+++ b/main/COMO.ipynb
@@ -232,15 +232,10 @@
    ]
   },
   {
+   "metadata": {},
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-09T22:10:43.421233Z",
-     "start_time": "2024-12-09T22:10:43.418100Z"
-    }
-   },
    "outputs": [],
+   "execution_count": null,
    "source": [
     "from pathlib import Path\n",
     "\n",
@@ -249,14 +244,20 @@
     "\n",
     "taxon_id = 9606\n",
     "context_names = [\"naiveB\"]\n",
-    "gene_info_filepath = [Path(f\"data/results/{context}/gene_info.csv\") for context in context_names]\n",
-    "como_context_dir = [Path(f\"data/COMO_input/{context}\") for context in context_names]\n",
-    "trna_matrix_filepath = [Path(f\"data/results/{context}/total-rna/totalrna_{context}.csv\") for context in context_names]\n",
-    "polya_matrix_filepath = [Path(f\"data/results/{context}/polya-rna/polyarna_{context}.csv\") for context in context_names]\n",
+    "\n",
+    "gene_info_filepath = {context: Path(f\"data/results/{context}/gene_info.csv\") for context in context_names}\n",
+    "como_context_dir = {context: Path(f\"data/COMO_input/{context}\") for context in context_names}\n",
+    "trna_matrix_filepath = {context: Path(f\"data/results/{context}/total-rna/totalrna_{context}.csv\") for context in context_names}\n",
+    "mrna_matrix_filepath = {context: Path(f\"data/results/{context}/polya-rna/polyarna_{context}.csv\") for context in context_names}\n",
+    "proteomics_matrix_filepath = {context: Path(f\"data/data_matrices/{context}/protein_abundance_{context}.csv\") for context in context_names}\n",
+    "\n",
+    "trna_metadata_filepath = Path(\"data/config_sheets/trna_config.xlsx\")\n",
+    "mrna_metadata_filepath = Path(\"data/config_sheets/mrna_config.xlsx\")\n",
+    "proteomics_metadata_filepath = Path(\"data/config_sheets/proteomics_config.xlsx\")\n",
     "\n",
     "# No single-cell data is provided by default; COMO accepts single-cell data in CSV or h5ad format\n",
     "# If you are using single-cell data, adjust the following lines accordingly\n",
-    "scrna_matrix_filepath = [Path(f\"data/results/{context}/scrna/scrna_{context}.csv\") for context in context_names]\n",
+    "scrna_matrix_filepath = {context: Path(f\"data/results/{context}/scrna/scrna_{context}.csv\") for context in context_names}\n",
     "# scrna_matrix_filepath = [Path(f\"data/results/{context}/scrna/scrna_{context}.h5ad\") for context in context_names]\n"
    ]
   },
@@ -272,37 +273,24 @@
    ]
   },
   {
+   "metadata": {},
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-07T03:30:27.253112Z",
-     "start_time": "2024-12-07T03:30:27.236557Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001B[32m2024-12-09 16:23:55\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m274\u001B[0m - \u001B[32m\u001B[1mWrote gene count matrix for 'polya' RNA at '/Users/joshl/Projects/COMO/main/data/results/naiveB/polya-rna/polyarna_naiveB.csv'\u001B[0m\n",
-      "\u001B[32m2024-12-09 16:23:55\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m274\u001B[0m - \u001B[32m\u001B[1mWrote gene count matrix for 'total' RNA at '/Users/joshl/Projects/COMO/main/data/results/naiveB/total-rna/totalrna_naiveB.csv'\u001B[0m\n",
-      "\u001B[32m2024-12-09 16:23:55\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m451\u001B[0m - \u001B[1mFetching gene info (this may take 1-5 minutes)\u001B[0m\n",
-      "\u001B[32m2024-12-09 16:24:13\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m488\u001B[0m - \u001B[32m\u001B[1mGene Info file written at '/Users/joshl/Projects/COMO/main/data/results/naiveB/gene_info.csv'\u001B[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
+   "execution_count": null,
    "source": [
-    "for i in range(len(context_names)):\n",
+    "for context in context_names:\n",
+    "    if context not in {*trna_matrix_filepath, *mrna_matrix_filepath}:\n",
+    "        continue\n",
     "    await rnaseq_preprocess(\n",
-    "        context_name=context_names[i],\n",
+    "        context_name=context,\n",
     "        taxon=taxon_id,\n",
-    "        output_gene_info_filepath=gene_info_filepath[i],\n",
-    "        como_context_dir=como_context_dir[i],\n",
-    "        output_trna_config_filepath=Path(\"./data/config_sheets/trna_config.xlsx\"),\n",
-    "        output_trna_count_matrix_filepath=trna_matrix_filepath[i],\n",
-    "        output_mrna_config_filepath=Path(\"./data/config_sheets/polya_config.xlsx\"),\n",
-    "        output_mrna_count_matrix_filepath=polya_matrix_filepath[i],\n",
+    "        como_context_dir=como_context_dir[context],\n",
+    "        input_matrix_filepath=None,\n",
+    "        output_gene_info_filepath=gene_info_filepath[context],\n",
+    "        output_trna_config_filepath=trna_metadata_filepath,\n",
+    "        output_trna_count_matrix_filepath=trna_matrix_filepath[context],\n",
+    "        output_mrna_config_filepath=mrna_metadata_filepath,\n",
+    "        output_mrna_count_matrix_filepath=mrna_matrix_filepath[context],\n",
     "        cache=True,\n",
     "        log_level=\"INFO\",\n",
     "    )"
@@ -379,68 +367,10 @@
    ]
   },
   {
+   "metadata": {},
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-12-09T22:13:42.060657Z",
-     "start_time": "2024-12-09T22:13:41.740347Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001B[32m2024-12-09 16:18:43.958\u001B[0m | \u001B[34m\u001B[1mDEBUG   \u001B[0m | \u001B[36mcomo.rnaseq_gen\u001B[0m:\u001B[36mrnaseq_gen\u001B[0m:\u001B[36m805\u001B[0m - \u001B[34m\u001B[1mStarting 'naiveB'\u001B[0m\n",
-      "\u001B[32m2024-12-09 16:18:43.961\u001B[0m | \u001B[34m\u001B[1mDEBUG   \u001B[0m | \u001B[36mcomo.rnaseq_gen\u001B[0m:\u001B[36m_read_counts\u001B[0m:\u001B[36m175\u001B[0m - \u001B[34m\u001B[1mReading CSV file at 'data/results/naiveB/total-rna/totalrna_naiveB.csv'\u001B[0m\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "       entrez_gene_id  expressed  high\n",
-      "0                7105          0     0\n",
-      "1               64102          0     0\n",
-      "2                8813          0     0\n",
-      "3               57147          0     0\n",
-      "4               55732          0     0\n",
-      "...               ...        ...   ...\n",
-      "34396       124901321          0     0\n",
-      "34397       124902403          0     0\n",
-      "34398       101929614          0     0\n",
-      "34399       107984888          0     0\n",
-      "34400       124900697          0     0\n",
-      "\n",
-      "[34401 rows x 3 columns]\n"
-     ]
-    },
-    {
-     "ename": "KeyError",
-     "evalue": "'ensembl_gene_id'",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[0;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
-      "File \u001B[0;32m~/Projects/COMO/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[0;34m(self, key)\u001B[0m\n\u001B[1;32m   3804\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m-> 3805\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_engine\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcasted_key\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   3806\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n",
-      "File \u001B[0;32mindex.pyx:167\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[0;34m()\u001B[0m\n",
-      "File \u001B[0;32mindex.pyx:196\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[0;34m()\u001B[0m\n",
-      "File \u001B[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[0;34m()\u001B[0m\n",
-      "File \u001B[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[0;34m()\u001B[0m\n",
-      "\u001B[0;31mKeyError\u001B[0m: 'ensembl_gene_id'",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001B[0;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
-      "Cell \u001B[0;32mIn[4], line 11\u001B[0m\n\u001B[1;32m      8\u001B[0m cutoff \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m3\u001B[39m\n\u001B[1;32m     10\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i, context \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(context_names):\n\u001B[0;32m---> 11\u001B[0m     \u001B[38;5;28;01mawait\u001B[39;00m rnaseq_gen(  \u001B[38;5;66;03m# noqa\u001B[39;00m\n\u001B[1;32m     12\u001B[0m         context_name\u001B[38;5;241m=\u001B[39mcontext,\n\u001B[1;32m     13\u001B[0m         input_rnaseq_filepath\u001B[38;5;241m=\u001B[39mtrna_matrix_filepath[i],\n\u001B[1;32m     14\u001B[0m         input_gene_info_filepath\u001B[38;5;241m=\u001B[39mgene_info_filepath[i],\n\u001B[1;32m     15\u001B[0m         output_rnaseq_filepath\u001B[38;5;241m=\u001B[39mtrna_matrix_filepath[i],\n\u001B[1;32m     16\u001B[0m         prep\u001B[38;5;241m=\u001B[39mRNAPrepMethod\u001B[38;5;241m.\u001B[39mTOTAL,\n\u001B[1;32m     17\u001B[0m         taxon\u001B[38;5;241m=\u001B[39mtaxon_id,\n\u001B[1;32m     18\u001B[0m         input_metadata_filepath\u001B[38;5;241m=\u001B[39mPath(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m./data/config_sheets/trna_config.xlsx\u001B[39m\u001B[38;5;124m\"\u001B[39m),\n\u001B[1;32m     19\u001B[0m         replicate_ratio\u001B[38;5;241m=\u001B[39mreplicate_ratio,\n\u001B[1;32m     20\u001B[0m         high_replicate_ratio\u001B[38;5;241m=\u001B[39mhigh_confidence_replicate_ratio,\n\u001B[1;32m     21\u001B[0m         batch_ratio\u001B[38;5;241m=\u001B[39mbatch_ratio,\n\u001B[1;32m     22\u001B[0m         high_batch_ratio\u001B[38;5;241m=\u001B[39mhigh_confidence_batch_ratio,\n\u001B[1;32m     23\u001B[0m         technique\u001B[38;5;241m=\u001B[39mtechnique,\n\u001B[1;32m     24\u001B[0m         cutoff\u001B[38;5;241m=\u001B[39mcutoff\n\u001B[1;32m     25\u001B[0m     )\n",
-      "File \u001B[0;32m~/Projects/COMO/main/como/rnaseq_gen.py:808\u001B[0m, in \u001B[0;36mrnaseq_gen\u001B[0;34m(context_name, input_rnaseq_filepath, input_gene_info_filepath, output_rnaseq_filepath, prep, taxon, input_metadata_filepath, input_metadata_df, replicate_ratio, high_replicate_ratio, batch_ratio, high_batch_ratio, technique, cutoff)\u001B[0m\n\u001B[1;32m    805\u001B[0m logger\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mStarting \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext_name\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m    806\u001B[0m output_rnaseq_filepath\u001B[38;5;241m.\u001B[39mparent\u001B[38;5;241m.\u001B[39mmkdir(parents\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, exist_ok\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[0;32m--> 808\u001B[0m \u001B[38;5;28;01mawait\u001B[39;00m _save_rnaseq_tests(\n\u001B[1;32m    809\u001B[0m     context_name\u001B[38;5;241m=\u001B[39mcontext_name,\n\u001B[1;32m    810\u001B[0m     rnaseq_matrix\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mawait\u001B[39;00m _read_counts(input_rnaseq_filepath),\n\u001B[1;32m    811\u001B[0m     metadata_df\u001B[38;5;241m=\u001B[39minput_metadata_df \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28;01mawait\u001B[39;00m _create_metadata_df(input_metadata_filepath),\n\u001B[1;32m    812\u001B[0m     gene_info_df\u001B[38;5;241m=\u001B[39mpd\u001B[38;5;241m.\u001B[39mread_csv(input_gene_info_filepath),\n\u001B[1;32m    813\u001B[0m     output_filepath\u001B[38;5;241m=\u001B[39moutput_rnaseq_filepath,\n\u001B[1;32m    814\u001B[0m     prep\u001B[38;5;241m=\u001B[39mprep,\n\u001B[1;32m    815\u001B[0m     taxon\u001B[38;5;241m=\u001B[39mtaxon,\n\u001B[1;32m    816\u001B[0m     replicate_ratio\u001B[38;5;241m=\u001B[39mreplicate_ratio,\n\u001B[1;32m    817\u001B[0m     batch_ratio\u001B[38;5;241m=\u001B[39mbatch_ratio,\n\u001B[1;32m    818\u001B[0m     high_replicate_ratio\u001B[38;5;241m=\u001B[39mhigh_replicate_ratio,\n\u001B[1;32m    819\u001B[0m     high_batch_ratio\u001B[38;5;241m=\u001B[39mhigh_batch_ratio,\n\u001B[1;32m    820\u001B[0m     technique\u001B[38;5;241m=\u001B[39mtechnique,\n\u001B[1;32m    821\u001B[0m     cut_off\u001B[38;5;241m=\u001B[39mcutoff,\n\u001B[1;32m    822\u001B[0m )\n",
-      "File \u001B[0;32m~/Projects/COMO/main/como/rnaseq_gen.py:671\u001B[0m, in \u001B[0;36m_save_rnaseq_tests\u001B[0;34m(context_name, rnaseq_matrix, metadata_df, gene_info_df, output_filepath, prep, taxon, replicate_ratio, batch_ratio, high_replicate_ratio, high_batch_ratio, technique, cut_off)\u001B[0m\n\u001B[1;32m    662\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Save the results of the RNA-Seq tests to a CSV file.\"\"\"\u001B[39;00m\n\u001B[1;32m    663\u001B[0m filtering_options \u001B[38;5;241m=\u001B[39m _FilteringOptions(\n\u001B[1;32m    664\u001B[0m     replicate_ratio\u001B[38;5;241m=\u001B[39mreplicate_ratio,\n\u001B[1;32m    665\u001B[0m     batch_ratio\u001B[38;5;241m=\u001B[39mbatch_ratio,\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    668\u001B[0m     high_batch_ratio\u001B[38;5;241m=\u001B[39mhigh_batch_ratio,\n\u001B[1;32m    669\u001B[0m )\n\u001B[0;32m--> 671\u001B[0m read_counts_results: _ReadMatrixResults \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m _build_matrix_results(\n\u001B[1;32m    672\u001B[0m     matrix\u001B[38;5;241m=\u001B[39mrnaseq_matrix,\n\u001B[1;32m    673\u001B[0m     gene_info\u001B[38;5;241m=\u001B[39mgene_info_df,\n\u001B[1;32m    674\u001B[0m     metadata_df\u001B[38;5;241m=\u001B[39mmetadata_df,\n\u001B[1;32m    675\u001B[0m     taxon\u001B[38;5;241m=\u001B[39mtaxon,\n\u001B[1;32m    676\u001B[0m )\n\u001B[1;32m    677\u001B[0m metrics \u001B[38;5;241m=\u001B[39m read_counts_results\u001B[38;5;241m.\u001B[39mmetrics\n\u001B[1;32m    678\u001B[0m entrez_gene_ids \u001B[38;5;241m=\u001B[39m read_counts_results\u001B[38;5;241m.\u001B[39mentrez_gene_ids\n",
-      "File \u001B[0;32m~/Projects/COMO/main/como/rnaseq_gen.py:201\u001B[0m, in \u001B[0;36m_build_matrix_results\u001B[0;34m(matrix, gene_info, metadata_df, taxon)\u001B[0m\n\u001B[1;32m    199\u001B[0m gene_info \u001B[38;5;241m=\u001B[39m gene_info_migrations(gene_info)\n\u001B[1;32m    200\u001B[0m \u001B[38;5;28mprint\u001B[39m(matrix)\n\u001B[0;32m--> 201\u001B[0m conversion \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m ensembl_to_gene_id_and_symbol(ids\u001B[38;5;241m=\u001B[39m\u001B[43mmatrix\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mensembl_gene_id\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241m.\u001B[39mtolist(), taxon\u001B[38;5;241m=\u001B[39mtaxon)\n\u001B[1;32m    202\u001B[0m matrix \u001B[38;5;241m=\u001B[39m matrix\u001B[38;5;241m.\u001B[39mmerge(conversion, on\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mensembl_gene_id\u001B[39m\u001B[38;5;124m\"\u001B[39m, how\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mleft\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m    204\u001B[0m \u001B[38;5;66;03m# Only include Entrez and Ensembl Gene IDs that are present in `gene_info`\u001B[39;00m\n",
-      "File \u001B[0;32m~/Projects/COMO/.venv/lib/python3.10/site-packages/pandas/core/frame.py:4102\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[0;34m(self, key)\u001B[0m\n\u001B[1;32m   4100\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcolumns\u001B[38;5;241m.\u001B[39mnlevels \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[1;32m   4101\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_getitem_multilevel(key)\n\u001B[0;32m-> 4102\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolumns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   4103\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_integer(indexer):\n\u001B[1;32m   4104\u001B[0m     indexer \u001B[38;5;241m=\u001B[39m [indexer]\n",
-      "File \u001B[0;32m~/Projects/COMO/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[0;34m(self, key)\u001B[0m\n\u001B[1;32m   3807\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(casted_key, \u001B[38;5;28mslice\u001B[39m) \u001B[38;5;129;01mor\u001B[39;00m (\n\u001B[1;32m   3808\u001B[0m         \u001B[38;5;28misinstance\u001B[39m(casted_key, abc\u001B[38;5;241m.\u001B[39mIterable)\n\u001B[1;32m   3809\u001B[0m         \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28many\u001B[39m(\u001B[38;5;28misinstance\u001B[39m(x, \u001B[38;5;28mslice\u001B[39m) \u001B[38;5;28;01mfor\u001B[39;00m x \u001B[38;5;129;01min\u001B[39;00m casted_key)\n\u001B[1;32m   3810\u001B[0m     ):\n\u001B[1;32m   3811\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m InvalidIndexError(key)\n\u001B[0;32m-> 3812\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[1;32m   3813\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[1;32m   3814\u001B[0m     \u001B[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001B[39;00m\n\u001B[1;32m   3815\u001B[0m     \u001B[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001B[39;00m\n\u001B[1;32m   3816\u001B[0m     \u001B[38;5;66;03m#  the TypeError.\u001B[39;00m\n\u001B[1;32m   3817\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_indexing_error(key)\n",
-      "\u001B[0;31mKeyError\u001B[0m: 'ensembl_gene_id'"
-     ]
-    }
-   ],
+   "outputs": [],
+   "execution_count": null,
    "source": [
     "from como.rnaseq_gen import FilteringTechnique, rnaseq_gen\n",
     "\n",
@@ -451,15 +381,17 @@
     "technique = FilteringTechnique.zfpkm\n",
     "cutoff = -3\n",
     "\n",
-    "for i, context in enumerate(context_names):\n",
+    "for context in context_names:\n",
+    "    if context not in trna_matrix_filepath:\n",
+    "        continue\n",
     "    await rnaseq_gen(\n",
     "        context_name=context,\n",
-    "        input_rnaseq_filepath=trna_matrix_filepath[i],\n",
-    "        input_gene_info_filepath=gene_info_filepath[i],\n",
-    "        output_rnaseq_filepath=trna_matrix_filepath[i],\n",
+    "        input_rnaseq_filepath=trna_matrix_filepath[context],\n",
+    "        input_gene_info_filepath=gene_info_filepath[context],\n",
+    "        output_rnaseq_filepath=trna_matrix_filepath[context],\n",
     "        prep=RNAPrepMethod.TOTAL,\n",
     "        taxon=taxon_id,\n",
-    "        input_metadata_filepath=Path(\"./data/config_sheets/trna_config.xlsx\"),\n",
+    "        input_metadata_filepath=trna_metadata_filepath,\n",
     "        replicate_ratio=replicate_ratio,\n",
     "        high_replicate_ratio=high_confidence_replicate_ratio,\n",
     "        batch_ratio=batch_ratio,\n",
@@ -489,10 +421,10 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "from como.rnaseq_gen import FilteringTechnique, rnaseq_gen\n",
     "\n",
@@ -503,15 +435,17 @@
     "technique = FilteringTechnique.zfpkm\n",
     "cutoff = -3\n",
     "\n",
-    "for i, context in enumerate(context_names):\n",
+    "for context in context_names:\n",
+    "    if context not in mrna_matrix_filepath:\n",
+    "        continue\n",
     "    await rnaseq_gen(\n",
     "        context_name=context,\n",
-    "        input_rnaseq_filepath=polya_matrix_filepath[i],\n",
-    "        input_gene_info_filepath=gene_info_filepath[i],\n",
-    "        output_rnaseq_filepath=polya_matrix_filepath[i],\n",
+    "        input_rnaseq_filepath=mrna_matrix_filepath[context],\n",
+    "        input_gene_info_filepath=gene_info_filepath[context],\n",
+    "        output_rnaseq_filepath=mrna_matrix_filepath[context],\n",
     "        prep=RNAPrepMethod.MRNA,\n",
     "        taxon=taxon_id,\n",
-    "        input_metadata_filepath=Path(\"./data/config_sheets/mrna_config.xlsx\"),\n",
+    "        input_metadata_filepath=mrna_metadata_filepath,\n",
     "        replicate_ratio=replicate_ratio,\n",
     "        high_replicate_ratio=high_confidence_replicate_ratio,\n",
     "        batch_ratio=batch_ratio,\n",
@@ -541,10 +475,10 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "from como.rnaseq_gen import FilteringTechnique, rnaseq_gen\n",
     "\n",
@@ -555,12 +489,14 @@
     "technique = FilteringTechnique.umi\n",
     "cutoff = -3\n",
     "\n",
-    "for i, context in enumerate(context_names):\n",
+    "for context in context_names:\n",
+    "    if context not in scrna_matrix_filepath:\n",
+    "        continue\n",
     "    await rnaseq_gen(\n",
     "        context_name=context,\n",
-    "        input_rnaseq_filepath=scrna_matrix_filepath[i],\n",
-    "        input_gene_info_filepath=gene_info_filepath[i],\n",
-    "        output_rnaseq_filepath=scrna_matrix_filepath[i],\n",
+    "        input_rnaseq_filepath=scrna_matrix_filepath[context],\n",
+    "        input_gene_info_filepath=gene_info_filepath[context],\n",
+    "        output_rnaseq_filepath=scrna_matrix_filepath[context],\n",
     "        prep=RNAPrepMethod.SCRNA,\n",
     "        taxon=taxon_id,\n",
     "        input_metadata_filepath=Path(\"./data/config_sheets/scrna_config.xlsx\"),\n",
@@ -590,38 +526,34 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
-    "proteomics_config_file = \"proteomics_data_inputs_paper.xlsx\"\n",
-    "rep_ratio = 0.75\n",
-    "batch_ratio = 0.75\n",
-    "high_rep_ratio = 1.0\n",
-    "high_batch_ratio = 1.0\n",
-    "quantile = 25\n",
-    "\n",
-    "# fmt: off\n",
-    "cmd = \" \".join(\n",
-    "    [\n",
-    "        \"python3\", \"como/proteomics_gen.py\",\n",
-    "        \"--config-file\", proteomics_config_file,\n",
-    "        \"--replicate-ratio\", str(rep_ratio),\n",
-    "        \"--high-replicate-ratio\", str(high_rep_ratio),\n",
-    "        \"--batch-ratio\", str(batch_ratio),\n",
-    "        \"--high-batch-ratio\", str(high_batch_ratio),\n",
-    "        \"--quantile\", str(quantile),\n",
-    "    ]\n",
-    ")\n",
-    "# fmt: on\n",
-    "\n",
-    "!{cmd}"
+    "from como.proteomics_gen import proteomics_gen\n",
+    "\n",
+    "for context in context_names:\n",
+    "    await proteomics_gen(\n",
+    "        context_name=context_names,\n",
+    "        config_filepath=proteomics_metadata_filepath,\n",
+    "        matrix_filepath=proteomics_matrix_filepath[context],\n",
+    "        output_boolean_filepath=Path(f\"data/results/{context}/proteomics/{context}_proteomics_boolean_matrix.csv\"),\n",
+    "        output_gaussian_png_filepath=Path(f\"data/results/{context}/proteomics/{context}_proteomics_gaussian.png\"),\n",
+    "        output_gaussian_html_filepath=Path(f\"data/results/{context}/proteomics/{context}_proteomics_gaussian.html\"),\n",
+    "        output_z_score_matrix_filepath=Path(f\"data/results/{context}/proteomics/{context}_zscore_matrix.csv\"),\n",
+    "        input_entrez_map=Path(f\"data/results/{context}/proteomics/{context}_entrez_map.csv\"),\n",
+    "        replicate_ratio=0.5,\n",
+    "        batch_ratio=0.5,\n",
+    "        high_confidence_replicate_ratio=0.7,\n",
+    "        high_confidence_batch_ratio=0.7,\n",
+    "        quantile=25,\n",
+    "    )"
    ]
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "# Cluster Sample Data (Optional)\n",
     "This step is used to cluster the samples based on their expression values. This can be used to determine which samples are more similar to each other. In a perfect world, one cluster would be created for each context type used. This is done using the `como/cluster_rnaseq.py` script.\n",
@@ -645,10 +577,10 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "filt_technique = \"zfpkm\"\n",
     "cluster_algorithm = \"umap\"\n",
diff --git a/main/como/proteomics_gen.py b/main/como/proteomics_gen.py
index 9a53304e..d020d71d 100644
--- a/main/como/proteomics_gen.py
+++ b/main/como/proteomics_gen.py
@@ -47,7 +47,8 @@ async def load_gene_symbol_map(gene_symbols: list[str], entrez_map: Path | None
 def abundance_to_bool_group(
     context_name,
     abundance_filepath: Path,
-    output_gaussian_img_filepath: Path,
+    output_gaussian_png_filepath: Path,
+    output_gaussian_html_filepath: Path,
     output_z_score_matrix_filepath: Path,
     abundance_matrix: pd.DataFrame,
     replicate_ratio: float,
@@ -59,7 +60,8 @@ def abundance_to_bool_group(
     abundance_matrix.to_csv(abundance_filepath, index_label="entrez_gene_id")
     protein_transform_main(
         abundance_df=abundance_matrix,
-        output_gaussian_img_filepath=output_gaussian_img_filepath,
+        output_gaussian_png_filepath=output_gaussian_png_filepath,
+        output_gaussian_html_filepath=output_gaussian_html_filepath,
         output_z_score_matrix_filepath=output_z_score_matrix_filepath,
     )
 
@@ -147,13 +149,14 @@ async def proteomics_gen(
     config_filepath: Path,
     matrix_filepath: Path,
     output_boolean_filepath: Path,
-    output_gaussian_img_filepath: Path,
     output_z_score_matrix_filepath: Path,
+    output_gaussian_png_filepath: Path | None = None,
+    output_gaussian_html_filepath: Path | None = None,
     input_entrez_map: Path | None = None,
     replicate_ratio: float = 0.5,
     batch_ratio: float = 0.5,
     high_confidence_replicate_ratio: float = 0.7,
-    high_confience_batch_ratio: float = 0.7,
+    high_confidence_batch_ratio: float = 0.7,
     quantile: int = 25,
 ):
     """Generate proteomics data."""
@@ -203,12 +206,13 @@ async def proteomics_gen(
             high_confidence_replicate_ratio=high_confidence_replicate_ratio,
             quantile=quantile,
             output_boolean_filepath=output_boolean_filepath,
-            output_gaussian_img_filepath=output_gaussian_img_filepath,
+            output_gaussian_png_filepath=output_gaussian_png_filepath,
+            output_gaussian_html_filepath=output_gaussian_html_filepath,
             output_z_score_matrix_filepath=output_z_score_matrix_filepath,
         )
     to_bool_context(
         context_name=context_name,
         group_ratio=batch_ratio,
-        hi_group_ratio=high_confience_batch_ratio,
+        hi_group_ratio=high_confidence_batch_ratio,
         group_names=groups,
     )
diff --git a/main/como/proteomics_preprocessing.py b/main/como/proteomics_preprocessing.py
index 2fee5290..1329406d 100644
--- a/main/como/proteomics_preprocessing.py
+++ b/main/como/proteomics_preprocessing.py
@@ -131,7 +131,8 @@ def plot_gaussian_fit(z_results: ZResult, facet_titles: bool = True, x_min: int
 # Main function for protein abundance transformation
 def protein_transform_main(
     abundance_df: pd.DataFrame | str | Path,
-    output_gaussian_img_filepath: Path,
+    output_gaussian_png_filepath: Path,
+    output_gaussian_html_filepath: Path,
     output_z_score_matrix_filepath: Path,
 ) -> None:
     """Transform protein abundance data."""
@@ -142,9 +143,13 @@ def protein_transform_main(
     z_transform: ZResult = z_score_calc(abundance_df, min_thresh=0)
 
     fig = plot_gaussian_fit(z_results=z_transform, facet_titles=True, x_min=-4)
-    fig.write_image(output_gaussian_img_filepath.with_suffix(".png"))
-    fig.write_html(output_gaussian_img_filepath.with_suffix(".html"))
-    logger.info(f"Gaussian fit figure written to {output_gaussian_img_filepath}")
+
+    if output_gaussian_png_filepath:
+        fig.write_image(output_gaussian_png_filepath.with_suffix(".png"))
+        logger.info(f"PNG gaussian figure written to {output_gaussian_png_filepath}")
+    if output_gaussian_html_filepath:
+        fig.write_html(output_gaussian_html_filepath.with_suffix(".html"))
+        logger.info(f"Interactive HTML gaussian figure written to {output_gaussian_png_filepath}")
 
     z_transformed_abundances = z_transform.zfpkm
     z_transformed_abundances[abundance_df == 0] = -4

From 4c381ca5def51ba8310d1441eadbafe9c84fb40e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Wed, 11 Dec 2024 16:03:31 -0600
Subject: [PATCH 092/345] fix: no longer require rna processing type

---
 main/como/rnaseq_preprocess.py | 11 +----------
 1 file changed, 1 insertion(+), 10 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 02702b38..83eb504f 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -578,7 +578,6 @@ async def rnaseq_preprocess(
     output_gene_info_filepath: Path,
     como_context_dir: Path | None = None,
     input_matrix_filepath: Path | list[Path] | None = None,
-    preparation_method: RNAPrepMethod | list[RNAPrepMethod] | None = None,
     output_trna_config_filepath: Path | None = None,
     output_mrna_config_filepath: Path | None = None,
     output_trna_count_matrix_filepath: Path | None = None,
@@ -601,8 +600,7 @@ async def rnaseq_preprocess(
     :param output_mrna_count_matrix_filepath: The path to write messenger RNA count matrices
     :param como_context_dir: If in "create" mode, the input path(s) to the COMO_input directory of the current context
         i.e., the directory containing "fragmentSizes", "geneCounts", "insertSizeMetrics", etc. directories
-    :param input_matrix_filepath: If in "provide" mode, the path(s) to the count matrices to be processed
-    :param preparation_method: The preparation method
+    :param input_matrix_filepath: If in "provide" mode, the path(s) to the count matrices to be processed~
     :param cache: Should HTTP requests be cached
     :param log_level: The logging level
     :param log_location: The logging location
@@ -636,13 +634,6 @@ async def rnaseq_preprocess(
     )
 
     input_matrix_filepath = _listify(input_matrix_filepath)
-    preparation_method = _listify(preparation_method)
-
-    if len(input_matrix_filepath) != len(preparation_method):
-        raise ValueError(
-            "input_matrix_filepath (--input-matrix-filepath) and "
-            "preparation_method (--preparation-method) must be the same length."
-        )
     await _process(
         context_name=context_name,
         taxon=taxon,

From 7543065cbff3a20a0b3f69685512ef5f3171d268 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Thu, 12 Dec 2024 13:52:26 -0600
Subject: [PATCH 093/345] feat: add gurobi as an optional dependency

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 pyproject.toml | 34 ++++++++++++++++------------------
 1 file changed, 16 insertions(+), 18 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index fabd6c69..aa9fff60 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,3 +1,7 @@
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
 [project]
 name = "COMO"
 dynamic = ["version"]
@@ -8,7 +12,6 @@ dependencies = [
     "cobamp@git+https://github.com/JoshLoecker/cobamp@master",
     "cobra>=0.28.0",
     "fast-bioservices>=0.3.9",
-    "gurobipy>=11.0",
     "kaleido==0.2.1",
     "loguru>=0.7.2",
     "openpyxl>=3.1.5",
@@ -24,14 +27,21 @@ dependencies = [
 ]
 
 [project.optional-dependencies]
-interactive = [
+gurobi = [ "gurobipy>=10.0.0" ]
+interactive =   [
     "ipython>=8.0.0",
-    "jupyterlab>=4.3.2",
+    "jupyterlab>=4.3.2"
+]
+dev = [
+    "commitlint>=1.3.0",
+    "pytest-asyncio>=0.24.0",
+    "pytest>=8.3.3",
+    "ruff>=0.8.0",
+    "hypothesis>=6.122.1",
+    "pytest-cov>=6.0.0",
+    "commitizen>=4.1.0",
 ]
 
-[build-system]
-requires = ["hatchling"]
-build-backend = "hatchling.build"
 
 [tool.hatch.version]
 path = "main/como/__init__.py"
@@ -45,18 +55,6 @@ allow-direct-references = true
 [tool.pytest.ini_options]
 pythonpath = [ "main/src" ]
 
-[tool.uv]
-dev-dependencies = [
-    "commitlint>=1.3.0",
-    "pytest-asyncio>=0.24.0",
-    "pytest>=8.3.3",
-    "ruff>=0.8.0",
-    "hypothesis>=6.122.1",
-    "pytest-cov>=6.0.0",
-    "commitizen>=4.1.0",
-    "cz-conventional-gitmoji>=0.6.1",
-]
-
 [tool.commitizen]
 name = "cz_conventional_commits"
 tag_format = "$version"

From 875d0a46667924b67b9121cf528df99d4daca9c1 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 13 Dec 2024 10:32:23 -0600
Subject: [PATCH 094/345] fix: import the correct variable

---
 main/como/merge_xomics.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 8dc2a6fd..0632d0df 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -16,8 +16,8 @@
 
 from como import proteomics_gen, return_placeholder_data
 from como.combine_distributions import _combine_zscores
-from como.custom_types import RNAPrepMethod
 from como.project import Config
+from como.types import RNAPrepMethod
 from como.utils import split_gene_expression_data
 
 

From 0c0d991eeee36221e4907364446c19394e48d9c3 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 13 Dec 2024 10:32:44 -0600
Subject: [PATCH 095/345] fix: divide by zero error; grouping too many items

---
 main/como/rnaseq_gen.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 364bd4c3..7a9a5041 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -237,8 +237,8 @@ def calculate_tpm(metrics: NamedMetrics) -> NamedMetrics:
 
 def calculate_fpkm(metrics: NamedMetrics) -> NamedMetrics:
     """Calculate the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) for each sample in the metrics dictionary."""  # noqa: E501
-    matrix_values = []
     for study in metrics:
+        matrix_values = []
         for sample in range(metrics[study].num_samples):
             layout = metrics[study].layout[sample]
             count_matrix: npt.NDArray = metrics[study].count_matrix.iloc[:, sample].values
@@ -251,7 +251,7 @@ def calculate_fpkm(metrics: NamedMetrics) -> NamedMetrics:
                 case LayoutMethod.paired_end:  # FPKM
                     mean_fragment_lengths = metrics[study].fragment_lengths[sample]
                     # Ensure non-negative value
-                    effective_length = [max(0, size - (mean_fragment_lengths + 1)) for size in gene_size]
+                    effective_length = [max(1e-9, size - (mean_fragment_lengths + 1)) for size in gene_size]
                     n = count_matrix.sum()
                     fpkm = ((count_matrix + 1) * 1e9) / (np.array(effective_length) * n)
                     matrix_values.append(fpkm)

From fdb66dc06761b3a9eaa5ad961a921f9583b35757 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:10:02 -0600
Subject: [PATCH 096/345] style: rearrange code

---
 main/como/merge_xomics.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 0632d0df..829503b9 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -22,24 +22,24 @@
 
 
 class _MergedHeaderNames:
-    PROTEOMICS = "prote"
     TRNASEQ = "trnaseq"
     MRNASEQ = "mrnaseq"
     SCRNASEQ = "scrnaseq"
+    PROTEOMICS = "prote"
 
 
 class _ExpressedHeaderNames:
-    PROTEOMICS = f"{_MergedHeaderNames.PROTEOMICS}_exp"
     TRNASEQ = f"{_MergedHeaderNames.TRNASEQ}_exp"
     MRNASEQ = f"{_MergedHeaderNames.MRNASEQ}_exp"
     SCRNASEQ = f"{_MergedHeaderNames.SCRNASEQ}_exp"
+    PROTEOMICS = f"{_MergedHeaderNames.PROTEOMICS}_exp"
 
 
 class _HighExpressionHeaderNames:
-    PROTEOMICS = f"{_MergedHeaderNames.PROTEOMICS}_high"
     TRNASEQ = f"{_MergedHeaderNames.TRNASEQ}_high"
     MRNASEQ = f"{_MergedHeaderNames.MRNASEQ}_high"
     SCRNASEQ = f"{_MergedHeaderNames.SCRNASEQ}_high"
+    PROTEOMICS = f"{_MergedHeaderNames.PROTEOMICS}_high"
 
 
 class AdjustmentMethod(Enum):

From 43afd4f65d08be342a736e4dbe0ab7bd0a4c4c03 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:11:36 -0600
Subject: [PATCH 097/345] refactor: remove command line interface

---
 main/como/merge_xomics.py | 248 --------------------------------------
 1 file changed, 248 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 829503b9..cc875044 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -58,41 +58,6 @@ def from_string(cls, value: str) -> AdjustmentMethod:
         return cls(value)
 
 
-@dataclass
-class _Arguments:
-    trnaseq_filename: str
-    mrnaseq_filename: str
-    scrnaseq_filename: str
-    proteomics_filename: str
-    expression_requirement: str | int
-    adjustment_method: AdjustmentMethod
-    no_high_confidence: bool
-    no_na: bool
-    merge_zfpkm_distribution: bool
-    keep_transcriptomics_scores: bool
-    trna_weight: int
-    mrna_weight: int
-    scrna_weight: int
-    proteomics_weight: int
-    custom_expression_filename: str
-
-    def __post_init__(self):
-        if self.expression_requirement.isdigit():
-            self.expression_requirement = int(self.expression_requirement)
-            if self.expression_requirement < 1:
-                logger.warning(
-                    f"Expression requirement should be at least 1, setting to 1 now. Got {self.expression_requirement}"
-                )
-                self.expression_requirement = 1
-        elif self.expression_requirement != "default":
-            raise ValueError(
-                f"Expression requirement should be an integer or 'default', got {self.expression_requirement}"
-            )
-
-        if self.adjustment_method.value.lower() not in ["progressive", "regressive", "flat", "custom"]:
-            raise ValueError("Adjust method must be either 'progressive', 'regressive', 'flat', or 'custom'")
-
-
 def _load_rnaseq_tests(filename, context_name, prep_method: RNAPrepMethod) -> tuple[str, pd.DataFrame]:
     """Load rnaseq results.
 
@@ -627,225 +592,12 @@ async def merge_xomics(
         merge_zfpkm_distribution,
         keep_transcriptomics_score,
     )
-
-
-def _parse_args() -> _Arguments:
-    """Merge expression tables of multiple sources, (RNA-seq, proteomics) into one list.
-
-    User can specify the number of sources with an active gene in order for it to be considered active in the model.
-    Otherwise, it defaults to the number of sources provided. High-confidence genes from any source will be considered
-    active in the model, regardless of agreement with other sources.
-    """
-    parser = argparse.ArgumentParser(
-        prog="merge_xomics.py",
-        description="Merge expression tables of multiple sources (RNA-seq, proteomics) into one",
-        epilog="For additional help, please post questions/issues in the MADRID GitHub repo at "
-        "https://github.com/HelikarLab/MADRID or email babessell@gmail.com",
-    )
-
-    parser.add_argument(
-        "-d",
-        "--merge-zfpkm-distribution",
-        action="store_true",
-        required=False,
-        default=False,
-        dest="merge_zfpkm_distribution",
-        help="Flag to merge zFPKM distributions. Required if using iMAT reconstruction algorithm in "
-        "create_context_specific_model.py. Must have run rnaseq_gen.py with 'zFPKM' as "
-        "'--technique'. If --proteomics-config-file is given will merge proteomics distributions "
-        "with zFPKM distributions using a weighted scheme.",
     )
-
-    parser.add_argument(
-        "-k",
-        "--keep-transcriptomics-scores",
-        action="store_true",
-        required=False,
-        default=True,
-        dest="keep_transcriptomics_scores",
-        help="When merging z-score distributions of expression, if using both protein abundance and transcipt zFPKM "
-        "flag true if you wish to keep z-score of genes with no protein data, flag false if you wish to discard "
-        "and treat as no expression",
     )
-
-    parser.add_argument(
-        "-t",
-        "--total-rnaseq-config-file",
-        type=str,
-        required=False,
-        default=None,
-        dest="trnaseq_filename",
-        help="Name of total RNA-seq config .xlsx file in the /main/data/config_files/.",
     )
 
-    parser.add_argument(
-        "-m",
-        "--mrnaseq-config-file",
-        type=str,
-        required=False,
-        default=None,
-        dest="mrnaseq_filename",
-        help="Name of mRNA-seq config .xlsx file in the /main/data/config_files/.",
     )
-
-    parser.add_argument(
-        "-s",
-        "--scrnaseq-config-file",
-        type=str,
-        required=False,
-        default=None,
-        dest="scrnaseq_filename",
-        help="Name of RNA-seq config .xlsx file in the /main/data/config_files/.",
     )
-
-    parser.add_argument(
-        "-p",
-        "--proteomics-config-file",
-        type=str,
-        required=False,
-        default=None,
-        dest="proteomics_filename",
-        help="Name of proteomics config .xlsx file in the /main/data/config_files/.",
     )
-
-    parser.add_argument(
-        "-e",
-        "--expression-requirement",
-        type=str,
-        required=False,
-        default=None,
-        dest="expression_requirement",
-        help="Number of sources with active gene for it to be considered active "
-        "even if it is not a high confidence-gene",
     )
-
-    parser.add_argument(
-        "-r",
-        "--requirement-adjust",
-        type=str,
-        required=False,
-        default="flat",
-        dest="adjustment_method",
-        help="Technique to adjust expression requirement based on differences in number of provided data source types.",
-    )
-
-    parser.add_argument(
-        "-c",
-        "--custom-requirement-file",
-        required="custom" in sys.argv,  # required if --requriement-adjust is "custom",
-        dest="custom_expression_filename",
-        default=None,
-        help="Name of .xlsx file where first column is context names and "
-        "second column is expression requirement for that context",
-    )
-
-    parser.add_argument(
-        "--no-hc",
-        "--no-high-confidence",
-        action="store_true",
-        required=False,
-        default=False,
-        dest="no_high_confidence",
-        help="Prevent high-confidence genes forcing a gene to be used in final model, "
-        "irrespective of other other data sources",
     )
-
-    parser.add_argument(
-        "-na",
-        "--no-na-adjustment",
-        action="store_true",
-        required=False,
-        default=False,
-        dest="no_na",
-        help="Prevent genes missing in a data source library, but present in others from "
-        "subtracting 1 from the expression requirement per data source that gene is missing in",
-    )
-
-    parser.add_argument(
-        "-tw",
-        "--total-rnaseq-weight",
-        required=False,
-        default=1,
-        type=float,
-        dest="trna_weight",
-        help="Total RNA-seq weight for merging zFPKM distribution",
-    )
-
-    parser.add_argument(
-        "-mw",
-        "--mrnaseq-weight",
-        required=False,
-        default=1,
-        type=float,
-        dest="mrna_weight",
-        help="PolyA enriched (messenger) RNA-seq weight for merging zFPKM distribution",
-    )
-
-    parser.add_argument(
-        "-sw",
-        "--single-cell-rnaseq-weight",
-        required=False,
-        default=1,
-        type=float,
-        dest="scrna_weight",
-        help="Single-cell RNA-seq weight for merging zFPKM distribution",
-    )
-
-    parser.add_argument(
-        "-pw",
-        "--protein-weight",
-        required=False,
-        default=2,
-        type=float,
-        dest="proteomics_weight",
-        help="Proteomics weight for merging z-score distribution",
-    )
-    args = parser.parse_args()
-    args.adjustment_method = AdjustmentMethod.from_string(str(args.adjustment_method))
-    return _Arguments(**vars(args))
-
-
-if __name__ == "__main__":
-    import asyncio
-
-    args = _parse_args()
-    config = Config()
-
-    # read custom expression requirment file if used
-    if args.custom_expression_filename:
-        custom_filepath = config.data_dir / args.custom_expression_filename
-        custom_df = pd.read_excel(custom_filepath, sheet_name=0)
-        custom_df.columns = ["context", "req"]
-    else:
-        custom_df = pd.DataFrame([])
-
-    def_exp_req = sum(
-        test is None
-        for test in [
-            args.trnaseq_filename,
-            args.mrnaseq_filename,
-            args.scrnaseq_filename,
-            args.proteomics_filename,
-        ]
-    )
-
-    asyncio.run(
-        merge_xomics(
-            trnaseq_filepath=args.trnaseq_filename,
-            mrnaseq_filepath=args.mrnaseq_filename,
-            scrnaseq_filepath=args.scrnaseq_filename,
-            proteomics_filepath=args.proteomics_filename,
-            trna_weight=args.trna_weight,
-            mrna_weight=args.mrna_weight,
-            scrna_weight=args.scrna_weight,
-            proteomics_weight=args.proteomics_weight,
-            expression_requirement=args.expression_requirement,
-            adjust_method=args.adjustment_method,
-            no_high_confidence=args.no_high_confidence,
-            no_na=args.no_na,
-            merge_zfpkm_distribution=args.merge_zfpkm_distribution,
-            keep_transcriptomics_score=args.keep_transcriptomics_scores,
-        )
-    )
-
-    logger.success("Finished merging!")

From ad6e68db4410d2e6cef8e4f9abd818ada6cee148 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:12:38 -0600
Subject: [PATCH 098/345] feat: use MyGene instead of BioDBNet

---
 main/como/merge_xomics.py | 76 ++++++++++-----------------------------
 1 file changed, 19 insertions(+), 57 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index cc875044..7bd60e9d 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -11,7 +11,7 @@
 
 import numpy as np
 import pandas as pd
-from fast_bioservices import BioDBNet, Input, Output
+from fast_bioservices.biothings.mygene import MyGene
 from loguru import logger
 
 from como import proteomics_gen, return_placeholder_data
@@ -182,7 +182,7 @@ def _merge_logical_table(df: pd.DataFrame):
     return df
 
 
-async def _get_transcriptmoic_details(merged_df: pd.DataFrame) -> pd.DataFrame:
+async def _get_transcriptmoic_details(merged_df: pd.DataFrame, taxon_id: int) -> pd.DataFrame:
     """Get details of transcriptomic data.
 
     This function will get the following details of transcriptomic data:
@@ -198,6 +198,7 @@ async def _get_transcriptmoic_details(merged_df: pd.DataFrame) -> pd.DataFrame:
     """
     # If _ExpressedHeaderNames.PROTEOMICS.value is in the dataframe, lower the required expression by 1
     # We are only trying to get details for transcriptomic data
+    transcriptomic_df: pd.DataFrame = merged_df.copy()
     if _ExpressedHeaderNames.PROTEOMICS in merged_df.columns:
         # Get the number of sources required for a gene to be marked "expressed"
         required_expression = merged_df["Required"].iloc[0]
@@ -225,62 +226,23 @@ async def _get_transcriptmoic_details(merged_df: pd.DataFrame) -> pd.DataFrame:
             "Active",
         ] = 1
 
-    else:
-        transcriptomic_df: pd.DataFrame = merged_df.copy()
-
-    biodbnet = BioDBNet()
-    gene_details: pd.DataFrame = await biodbnet.async_db2db(
-        values=transcriptomic_df.index.astype(str).values.tolist(),
-        input_db=Input.GENE_ID,
-        output_db=[
-            Output.GENE_SYMBOL,
-            Output.ENSEMBL_GENE_INFO,
-            Output.GENE_INFO,
-        ],
-    )
-    gene_details["entrez_gene_id"] = gene_details.index
-    gene_details.reset_index(drop=True, inplace=True)
-
-    # Apply regex to search for "[Description: XXXXXX]" and retrieve the XXXXXX
-    # It excludes the square brackets and "Description: ", and only returns the description
-    # descriptions: list[str] = [
-    gene_details["description"] = [
-        i.group(1) if isinstance(i, re.Match) else "No Description Available"
-        for i in gene_details["Ensembl Gene Info"].apply(lambda x: re.search(r"\[Description: (.*)\]", x))
-    ]
-
-    gene_details["gene_info_type"] = [
-        i.group(1) if isinstance(i, re.Match) else "None"
-        for i in gene_details["Gene Info"].apply(lambda x: re.search(r"\[Gene Type: (.*)\]", x))
-    ]
-    gene_details["ensembl_info_type"] = [
-        i.group(1) if isinstance(i, re.Match) else "None"
-        for i in gene_details["Ensembl Gene Info"].apply(lambda x: re.search(r"\[Gene Type: (.*)\]", x))
-    ]
-
-    gene_type: list[str] = []
-    row: pd.DataFrame
-    for row in gene_details.itertuples():
-        if row.gene_info_type != "None":
-            gene_type.append(row.gene_info_type)
-        elif row.ensembl_info_type != "None":
-            gene_type.append(row.ensembl_info_type)
-        else:
-            gene_type.append("No Gene Type Available")
-    gene_details["gene_type"] = gene_type
-
-    # Drop gene_info_type and ensembl_info_type columns
-    gene_details.drop(
-        columns=[
-            "Ensembl Gene Info",
-            "Gene Info",
-            "gene_info_type",
-            "ensembl_info_type",
-        ],
-        inplace=True,
+    my_gene = MyGene()
+    gene_details: pd.DataFrame = pd.DataFrame(
+        data=pd.NA,
+        columns=["entrez_gene_id", "gene_symbol", "description", "gene_type"],
+        index=list(range(len(transcriptomic_df))),
     )
-    gene_details.rename(columns={"entrez_gene_id": "Entrez Gene ID"}, inplace=True)
-
+    for i, detail in enumerate(
+        await my_gene.query(
+            items=transcriptomic_df["entrez_gene_id"].tolist(),
+            taxon=taxon_id,
+            scopes="entrezgene",
+        )
+    ):
+        gene_details.at[i, "entrez_gene_id"] = detail["entrezgene"]
+        gene_details.at[i, "gene_symbol"] = detail["symbol"]
+        gene_details.at[i, "description"] = detail["name"]
+        gene_details.at[i, "gene_type"] = detail["type_of_gene"]
     return gene_details
 
 

From 59e4cfea3ea8264e9a7516819a0af5ff9b183100 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:12:58 -0600
Subject: [PATCH 099/345] refactor: import required modules

---
 main/como/merge_xomics.py | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 7bd60e9d..c13ab702 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -1,11 +1,5 @@
 from __future__ import annotations
 
-import argparse
-import json
-import re
-import sys
-from collections import Counter
-from dataclasses import dataclass
 from enum import Enum
 from pathlib import Path
 
@@ -14,11 +8,17 @@
 from fast_bioservices.biothings.mygene import MyGene
 from loguru import logger
 
-from como import proteomics_gen, return_placeholder_data
-from como.combine_distributions import _combine_zscores
+from como import return_placeholder_data
+from como.combine_distributions import (
+    _BatchEntry,
+    _BatchNames,
+    _InputMatrices,
+    _new_combine_zscores,
+    _OutputCombinedSourceFilepath,
+    _SourceWeights,
+)
+from como.data_types import RNAPrepMethod
 from como.project import Config
-from como.types import RNAPrepMethod
-from como.utils import split_gene_expression_data
 
 
 class _MergedHeaderNames:

From 7a028f9571b69201c150326b18a017132dc00565 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:14:06 -0600
Subject: [PATCH 100/345] style: use pythonic variable names

---
 main/como/merge_xomics.py | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index c13ab702..23b45d62 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -201,11 +201,11 @@ async def _get_transcriptmoic_details(merged_df: pd.DataFrame, taxon_id: int) ->
     transcriptomic_df: pd.DataFrame = merged_df.copy()
     if _ExpressedHeaderNames.PROTEOMICS in merged_df.columns:
         # Get the number of sources required for a gene to be marked "expressed"
-        required_expression = merged_df["Required"].iloc[0]
+        required_expression = merged_df["required"].iloc[0]
 
         # Subtract 1 from merged_df["TotalExpressed"] if the current value is greater than or equal to 1
         # This is done to take into account the removal of proteomic expression
-        merged_df["TotalExpressed"] = merged_df["TotalExpressed"].apply(lambda x: x - 1 if x >= 1 else x)
+        merged_df["total_expressed"] = merged_df["total_expressed"].apply(lambda x: x - 1 if x >= 1 else x)
 
         # Subtract required_expression by 1 if it is greater than 1
         if required_expression > 1:
@@ -222,8 +222,8 @@ async def _get_transcriptmoic_details(merged_df: pd.DataFrame, taxon_id: int) ->
         # Must recalculate TotalExpressed because proteomic data was removed
         # If the TotalExpressed column is less than the Required column, set active to 1, otherwise set it to 0
         transcriptomic_df.loc[
-            transcriptomic_df["TotalExpressed"] >= transcriptomic_df["Required"],
-            "Active",
+            transcriptomic_df["total_expressed"] >= transcriptomic_df["required"],
+            "active",
         ] = 1
 
     my_gene = MyGene()
@@ -332,16 +332,16 @@ async def _merge_xomics(
         )
         merge_data = prote_data if merge_data is None else merge_data.join(prote_data, how="outer")
 
-    if merge_data is None:
+    if merge_data.empty:
         logger.critical(
             f"No data is available for the '{context_name}' context. If this is intentional, ignore this error."
         )
         return {}
-    merge_data = _merge_logical_table(merge_data)
 
+    merge_data = _merge_logical_table(merge_data)
     num_sources = len(expression_list)
-    merge_data["Active"] = 0
-    merge_data["Required"] = 0
+    merge_data["active"] = 0
+    merge_data["required"] = 0
 
     if no_na:  # dont adjust for na values
         merge_data.loc[:, "Required"] = merge_data[expression_list].apply(
@@ -357,8 +357,6 @@ async def _merge_xomics(
         )
 
     # count number of sources gene is active in. Set to active in final output if at least adjusted expression reqirmnt
-    merge_data["TotalExpressed"] = merge_data[expression_list].sum(axis=1)
-    merge_data.loc[merge_data["TotalExpressed"] >= merge_data["Required"], "Active"] = 1
 
     if not no_hc:  # set genes that are high-confidence in at least one data source to active
         merge_data.loc[merge_data[high_confidence_list].sum(axis=1) > 0, "Active"] = 1
@@ -375,6 +373,8 @@ async def _merge_xomics(
     split_entrez = split_gene_expression_data(merge_data)
     split_entrez.to_csv(filepath, index_label="entrez_gene_id")
     files_dict = {context_name: filepath.as_posix()}
+    merge_data["total_expressed"] = merge_data[expression_list].sum(axis=1)
+    merge_data.loc[merge_data["total_expressed"] >= merge_data["required"], "active"] = 1
 
     transcriptomic_details = await _get_transcriptmoic_details(merge_data)
     transcriptomic_details_filepath = filepath.parent / f"TranscriptomicDetails_{context_name}.csv"

From 35de93e36cedab29d4a020ca5f1823418165e25c Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:15:17 -0600
Subject: [PATCH 101/345] refactor: remove encompassing for loop

Only a single context can be processed at a time; if more contexts need to be processed, merge xomics should be wrapped in a for loop
---
 main/como/merge_xomics.py | 207 ++++++++++++++++++++------------------
 1 file changed, 109 insertions(+), 98 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 23b45d62..87183a44 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -385,120 +385,131 @@ async def _merge_xomics(
     return files_dict
 
 
-async def _handle_context_batch(  # noqa: C901
-    trnaseq_file: Path | None,
-    mrnaseq_file: Path | None,
-    scrnaseq_file: Path | None,
-    proteomics_file: Path | None,
-    tweight: float,
-    mweight: float,
-    sweight: float,
-    pweight: float,
+async def _process(
+    *,
+    context_name: str,
+    trna_matrix: pd.DataFrame | None,
+    mrna_matrix: pd.DataFrame | None,
+    scrna_matrix: pd.DataFrame | None,
+    proteomic_matrix: pd.DataFrame | None,
+    trna_boolean_matrix: pd.DataFrame | None,
+    mrna_boolean_matrix: pd.DataFrame | None,
+    scrna_boolean_matrix: pd.DataFrame | None,
+    proteomic_boolean_matrix: pd.DataFrame | None,
+    trna_batches: dict[int, list[str]],
+    mrna_batches: dict[int, list[str]],
+    scrna_batches: dict[int, list[str]],
+    proteomic_batches: dict[int, list[str]],
+    trna_weight: int,
+    mrna_weight: int,
+    scrna_weight: int,
+    proteomic_weight: int,
+    taxon_id: int,
+    minimum_source_expression: int,
     expression_requirement: int,
+    weighted_z_floor: int,
+    weighted_z_ceiling: int,
     adjust_method: AdjustmentMethod,
-    no_hc: bool,
-    no_na: bool,
     merge_zfpkm_distribution: bool,
     keep_gene_score: bool,
+    force_activate_high_confidence: bool,
+    adjust_for_missing_sources: bool,
+    output_merge_activity_filepath: Path,
+    output_transcriptomic_details_filepath: Path,
+    output_trna_activity_filepath: Path | None,
+    output_mrna_activity_filepath: Path | None,
+    output_scrna_activity_filepath: Path | None,
+    output_proteomic_activity_filepath: Path | None,
+    output_final_model_scores_filepath: Path,
+    output_figure_dirpath: Path | None,
 ):
     """Merge different data sources for each context type."""
-    if all(file is None for file in [trnaseq_file, mrnaseq_file, scrnaseq_file, proteomics_file]):
-        raise ValueError("No configuration file was passed!")
-
-    config = Config()
-    sheet_names = []
-    for file in [trnaseq_file, mrnaseq_file, scrnaseq_file, proteomics_file]:
-        if file is not None:
-            config_filepath = config.config_dir / file
-            try:
-                xl = pd.ExcelFile(config_filepath, engine="openpyxl")
-            except Exception as e:
-                raise ValueError(f"Unable to read file '{config_filepath}'") from e
-            sheet_names += xl.sheet_names
-
-    use_trna = trnaseq_file is not None
-    use_mrna = mrnaseq_file is not None
-    use_scrna = scrnaseq_file is not None
-    use_proteins = proteomics_file is not None
-
-    counts = Counter(sheet_names)
-    sheet_names = sorted(set(sheet_names))
-    logger.info("Beginning to merge data within contexts")
-    dict_list = {}
-
-    max_inputs = max(counts.values())
-    min_inputs = min(counts.values())
+    num_sources = sum(1 for source in [trna_matrix, mrna_matrix, scrna_matrix, proteomic_matrix] if source is not None)
 
     if merge_zfpkm_distribution:
-        logger.debug("Using zFPKM distribution for merging")
-        _combine_zscores(
-            working_dir=config.result_dir.as_posix(),
-            context_names=sheet_names,
-            global_use_mrna=use_mrna,
-            global_use_trna=use_trna,
-            global_use_scrna=use_scrna,
-            global_use_proteins=use_proteins,
+        _new_combine_zscores(
+            context_name=context_name,
+            input_matrices=_InputMatrices(
+                trna=trna_matrix,
+                mrna=mrna_matrix,
+                scrna=scrna_matrix,
+                proteomics=proteomic_matrix,
+            ),
+            batch_names=_BatchNames(
+                trna=[_BatchEntry(batch_num=n, sample_names=s) for n, s in trna_batches.items()],
+                mrna=[_BatchEntry(batch_num=n, sample_names=s) for n, s in mrna_batches.items()],
+                scrna=[_BatchEntry(batch_num=n, sample_names=s) for n, s in scrna_batches.items()],
+                proteomics=[_BatchEntry(batch_num=n, sample_names=s) for n, s in proteomic_batches.items()],
+            ),
+            source_weights=_SourceWeights(
+                trna=trna_weight,
+                mrna=mrna_weight,
+                scrna=scrna_weight,
+                proteomics=proteomic_weight,
+            ),
+            output_filepaths=_OutputCombinedSourceFilepath(
+                trna=output_trna_activity_filepath,
+                mrna=output_mrna_activity_filepath,
+                scrna=output_scrna_activity_filepath,
+                proteomics=output_proteomic_activity_filepath,
+            ),
+            output_figure_dirpath=output_figure_dirpath,
+            output_final_model_scores=output_final_model_scores_filepath,
             keep_gene_scores=keep_gene_score,
-            global_trna_weight=tweight,
-            global_mrna_weight=mweight,
-            global_scrna_weight=sweight,
-            global_protein_weight=pweight,
+            weighted_z_floor=weighted_z_floor,
+            weighted_z_ceiling=weighted_z_ceiling,
         )
 
-    for context_name in sheet_names:
-        num_sources = counts[context_name]
-        match adjust_method:
-            case AdjustmentMethod.PROGRESSIVE:
-                adjusted_expression_requirement = (num_sources - min_inputs) + expression_requirement
-            case AdjustmentMethod.REGRESSIVE:
-                adjusted_expression_requirement = expression_requirement - (max_inputs - num_sources)
-            case AdjustmentMethod.FLAT:
-                adjusted_expression_requirement = expression_requirement
-            case _:
-                adjusted_expression_requirement = int(
-                    custom_df.iloc[custom_df["context"] == context_name, "req"].iloc[0]
-                )
-
-        if adjusted_expression_requirement != expression_requirement:
-            logger.debug(
-                f"Expression requirement of '{expression_requirement}' adjusted to "
-                f"'{adjusted_expression_requirement}' using '{adjust_method.value}' adjustment method "
-                f"for '{context_name}'."
-            )
+    # the more data sources available, the higher the expression requirement for the gene
+    if adjust_method == AdjustmentMethod.PROGRESSIVE:
+        adjusted_expression_requirement = (num_sources - minimum_source_expression) + expression_requirement
+    # the more data sources available, the lower the expression requirement for the gene
+    elif adjust_method == AdjustmentMethod.REGRESSIVE:
+        # we use a hardcoded 4 here because that is the maximum number of contexts available
+        # (trna, mrna, scrna, and proteomics is 4 sources)
+        adjusted_expression_requirement = expression_requirement - (4 - num_sources)
+    elif adjust_method == AdjustmentMethod.FLAT:
+        adjusted_expression_requirement = expression_requirement
+
+    if adjusted_expression_requirement != expression_requirement:
+        logger.debug(
+            f"Expression requirement of '{expression_requirement}' adjusted to "
+            f"'{adjusted_expression_requirement}' using '{adjust_method.value}' adjustment method "
+            f"for '{context_name}'."
+        )
 
-        if adjusted_expression_requirement > num_sources:
-            logger.warning(
-                f"Expression requirement for {context_name} was calculated to be greater "
-                f"than max number of input data sources. "
-                f"Will be force changed to {num_sources} to prevent output from having 0 active genes. "
-                f"Consider lowering the expression requirement or changing the adjustment method."
-            )
-            adjusted_expression_requirement = num_sources
+    if adjusted_expression_requirement > num_sources:
+        logger.warning(
+            f"Expression requirement for {context_name} was calculated to be greater "
+            f"than max number of input data sources. "
+            f"Will be force changed to {num_sources} to prevent output from having 0 active genes. "
+            f"Consider lowering the expression requirement or changing the adjustment method."
+        )
+        adjusted_expression_requirement = num_sources
 
-        if adjusted_expression_requirement < 1:  # never allow expression requirement to be less than one
-            logger.warning(
-                f"Expression requirement for {context_name} was calculated to be less than 1. "
-                "Will be changed to 1 to prevent output from having 0 active genes. "
-            )
-            adjusted_expression_requirement = 1
-
-        files_dict = await _merge_xomics(
-            context_name,
-            expression_requirement=adjusted_expression_requirement,
-            proteomics_file=proteomics_file,
-            trnaseq_file=trnaseq_file,
-            mrnaseq_file=mrnaseq_file,
-            scrnaseq_file=scrnaseq_file,
-            no_hc=no_hc,
-            no_na=no_na,
+    if adjusted_expression_requirement < 1:  # never allow expression requirement to be less than one
+        logger.warning(
+            f"Expression requirement for {context_name} was calculated to be less than 1. "
+            "Will be changed to 1 to prevent output from having 0 active genes. "
         )
+        adjusted_expression_requirement = 1
+
+    await _new_merge_xomics(
+        context_name=context_name,
+        expression_requirement=adjusted_expression_requirement,
+        trna_boolean_matrix=trna_boolean_matrix,
+        mrna_boolean_matrix=mrna_boolean_matrix,
+        scrna_boolean_matrix=scrna_boolean_matrix,
+        proteomic_boolean_matrix=proteomic_boolean_matrix,
+        output_merged_filepath=output_merge_activity_filepath,
+        output_gene_activity_filepath=output_final_model_scores_filepath,
+        output_transcriptomic_details_filepath=output_transcriptomic_details_filepath,
+        taxon_id=taxon_id,
+        force_activate_high_confidence=force_activate_high_confidence,
+        adjust_for_missing_sources=adjust_for_missing_sources,
+    )
 
-        dict_list |= files_dict
 
-    files_json = config.result_dir / "step1_results_files.json"
-    files_json.parent.mkdir(parents=True, exist_ok=True)
-    with files_json.open("w") as f:
-        json.dump(dict_list, f)  # type: ignore
 
     return
 

From 3adf9c686266ebd6a277942d4575fa6d3023ef1c Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:16:13 -0600
Subject: [PATCH 102/345] refactor: do not drop/set index

---
 main/como/merge_xomics.py | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 87183a44..3d2d0791 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -59,10 +59,7 @@ def from_string(cls, value: str) -> AdjustmentMethod:
 
 
 def _load_rnaseq_tests(filename, context_name, prep_method: RNAPrepMethod) -> tuple[str, pd.DataFrame]:
-    """Load rnaseq results.
-
-    Returns a dictionary of test (context, context, cell, etc ) names and rnaseq expression data
-    """
+    """Load rnaseq results."""
     config = Config()
 
     def load_dummy_dict():
@@ -111,7 +108,6 @@ def _merge_logical_table(df: pd.DataFrame):
     """
     # step 1: get all plural ENTREZ_GENE_IDs in the input table, extract unique IDs
 
-    df.reset_index(drop=False, inplace=True)
     df.dropna(axis=0, subset=["entrez_gene_id"], inplace=True)
     df["entrez_gene_id"] = df["entrez_gene_id"].astype(str).str.replace(" /// ", "//").astype(str)
 
@@ -164,7 +160,6 @@ def _merge_logical_table(df: pd.DataFrame):
     for merged_entrez_id, entrez_dups_list in entrez_dups_dict.items():
         df["entrez_gene_id"].replace(to_replace=entrez_dups_list, value=merged_entrez_id, inplace=True)
 
-    df.set_index("entrez_gene_id", inplace=True)
     df = df.fillna(-1).groupby(level=0).max()
     df.replace(-1, np.nan, inplace=True)
 

From 653e001c24bc8b42cfba61052c7ef8a763f3db85 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:17:21 -0600
Subject: [PATCH 103/345] refactor: use new one-context approach

---
 main/como/merge_xomics.py | 337 +++++++++++++++++++++-----------------
 1 file changed, 184 insertions(+), 153 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 3d2d0791..fcd35653 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -243,89 +243,36 @@ async def _get_transcriptmoic_details(merged_df: pd.DataFrame, taxon_id: int) ->
 
 async def _merge_xomics(
     context_name: str,
-    expression_requirement,
-    proteomics_file=None,
-    trnaseq_file=None,
-    mrnaseq_file=None,
-    scrnaseq_file=None,
-    no_hc=False,
-    no_na=False,
+    expression_requirement: int,
+    trna_boolean_matrix: pd.DataFrame | None,
+    mrna_boolean_matrix: pd.DataFrame | None,
+    scrna_boolean_matrix: pd.DataFrame | None,
+    proteomic_boolean_matrix: pd.DataFrame | None,
+    output_merged_filepath: Path,
+    output_gene_activity_filepath: Path,
+    output_transcriptomic_details_filepath: Path,
+    taxon_id: int,
+    force_activate_high_confidence: bool = True,
+    adjust_for_missing_sources: bool = False,
 ):
-    """Merge rnaseq and/or proteomics active genes.
-
-    :param proteomics_file: filename of proteomics config file in /main/data/config_sheets/
-    :param trnaseq_file: filename of Total RNA-seq config file in /main/data/config_sheets/
-    :param mrnaseq_file: filename of mRNA-seq config file in /main/data/config_sheets/
-    :param scrnaseq_file: filename of single-cell RNA-seq config file in /main/data/config_sheets/
-    :param no_hc: True if not adjusting for NA values (happens when gene missing from data source)
-    :param no_na: filename of single-cell RNA-seq config file in /main/data/config_sheets/
-    :param context_name: sheet name to use, should be context, context, cell type, etc
-    :param expression_requirement: integer, minimum number of provided sources with active gene for a it to be in model
-    :return: dictionary where keys are contexts, (tissue name, control type etc) and values are expression tables
-    """
-    config = Config()
-    logger.info(f"Merging data for {context_name}")
-    # load data for each source if it exists. IF not load an empty dummy dataset
-    trnaseq = _load_rnaseq_tests(filename=trnaseq_file, context_name=context_name, prep_method=RNAPrepMethod.TOTAL)
-    mrnaseq = _load_rnaseq_tests(filename=mrnaseq_file, context_name=context_name, prep_method=RNAPrepMethod.MRNA)
-    scrnaseq = _load_rnaseq_tests(filename=scrnaseq_file, context_name=context_name, prep_method=RNAPrepMethod.SCRNA)
-    proteomics = proteomics_gen.load_proteomics_tests(filename=proteomics_file, context_name=context_name)
-
-    expression_list = []
-    high_confidence_list = []
-    merge_data = None
-
-    if trnaseq[0] != "dummy":
-        expression_list.append(_ExpressedHeaderNames.TRNASEQ)
-        high_confidence_list.append(_HighExpressionHeaderNames.TRNASEQ)
-        trnaseq_data = trnaseq[1].loc[:, ["expressed", "high"]]
-        trnaseq_data.rename(
-            columns={
-                "expressed": _ExpressedHeaderNames.TRNASEQ,
-                "high": _HighExpressionHeaderNames.TRNASEQ,
-            },
-            inplace=True,
-        )
-        merge_data = trnaseq_data
-
-    if mrnaseq[0] != "dummy":
-        expression_list.append(_ExpressedHeaderNames.MRNASEQ)
-        high_confidence_list.append(_HighExpressionHeaderNames.MRNASEQ)
-        mrnaseq_data = mrnaseq[1].loc[:, ["expressed", "high"]]
-        mrnaseq_data.rename(
-            columns={
-                "expressed": _ExpressedHeaderNames.MRNASEQ,
-                "high": _HighExpressionHeaderNames.MRNASEQ,
-            },
-            inplace=True,
-        )
-        merge_data = mrnaseq_data if merge_data is None else merge_data.join(mrnaseq_data, how="outer")
-
-    if scrnaseq[0] != "dummy":
-        expression_list.append(_ExpressedHeaderNames.SCRNASEQ)
-        high_confidence_list.append(_HighExpressionHeaderNames.SCRNASEQ)
-        scrnaseq_data = scrnaseq[1].loc[:, ["expressed", "high"]]
-        scrnaseq_data.rename(
-            columns={
-                "expressed": _ExpressedHeaderNames.SCRNASEQ,
-                "high": _HighExpressionHeaderNames.SCRNASEQ,
-            },
-            inplace=True,
-        )
-        merge_data = scrnaseq_data if merge_data is None else merge_data.join(scrnaseq_data, how="outer")
-
-    if proteomics[0] != "dummy":
-        expression_list.append(_ExpressedHeaderNames.PROTEOMICS)
-        high_confidence_list.append(_HighExpressionHeaderNames.PROTEOMICS)
-        prote_data = proteomics[1].loc[:, ["expressed", "high"]]
-        prote_data.rename(
-            columns={
-                "expressed": _ExpressedHeaderNames.PROTEOMICS,
-                "high": _HighExpressionHeaderNames.PROTEOMICS,
-            },
-            inplace=True,
-        )
-        merge_data = prote_data if merge_data is None else merge_data.join(prote_data, how="outer")
+    expression_list: list[str] = []
+    high_confidence_list: list[str] = []
+    merge_data = pd.DataFrame()
+
+    for matrix, expressed_sourcetype, high_expressed_sourcetype in (
+        (trna_boolean_matrix, _ExpressedHeaderNames.TRNASEQ, _HighExpressionHeaderNames.TRNASEQ),
+        (mrna_boolean_matrix, _ExpressedHeaderNames.MRNASEQ, _HighExpressionHeaderNames.MRNASEQ),
+        (scrna_boolean_matrix, _ExpressedHeaderNames.SCRNASEQ, _HighExpressionHeaderNames.SCRNASEQ),
+        (proteomic_boolean_matrix, _ExpressedHeaderNames.PROTEOMICS, _HighExpressionHeaderNames.PROTEOMICS),
+    ):
+        if matrix is None:
+            continue
+
+        matrix: pd.DataFrame  # re-define type to assist in type hinting for IDEs
+        expression_list.append(expressed_sourcetype)
+        high_confidence_list.append(high_expressed_sourcetype)
+        matrix.rename(columns={"expressed": expressed_sourcetype, "high": high_expressed_sourcetype}, inplace=True)
+        merge_data = matrix if merge_data.empty else merge_data.merge(matrix, on="entrez_gene_id", how="outer")
 
     if merge_data.empty:
         logger.critical(
@@ -338,46 +285,31 @@ async def _merge_xomics(
     merge_data["active"] = 0
     merge_data["required"] = 0
 
-    if no_na:  # dont adjust for na values
-        merge_data.loc[:, "Required"] = merge_data[expression_list].apply(
-            lambda x: expression_requirement if (expression_requirement - (num_sources - x.count()) > 0) else 1,
-            axis=1,
-        )
-    else:  # subtract one from requirement per NA
-        merge_data.loc[:, "Required"] = merge_data[expression_list].apply(
+    if adjust_for_missing_sources:  # Subtract 1 from requirement per missing source
+        merge_data.loc[:, "required"] = merge_data[expression_list].apply(
             lambda x: expression_requirement - (num_sources - x.count())
             if (expression_requirement - (num_sources - x.count()) > 0)
             else 1,
             axis=1,
         )
+    else:  # Do not adjust for missing sources
+        merge_data.loc[:, "required"] = merge_data[expression_list].apply(
+            lambda x: expression_requirement if (expression_requirement - (num_sources - x.count()) > 0) else 1, axis=1
+        )
 
-    # count number of sources gene is active in. Set to active in final output if at least adjusted expression reqirmnt
-
-    if not no_hc:  # set genes that are high-confidence in at least one data source to active
-        merge_data.loc[merge_data[high_confidence_list].sum(axis=1) > 0, "Active"] = 1
-
-    # merge_data = merge_data.astype(int)
-    merge_data = merge_data
-
-    filepath = config.result_dir / context_name / f"merged_{context_name}.csv"
-    merge_data.to_csv(filepath, index_label="entrez_gene_id")
-
-    filepath = config.result_dir / context_name / f"ActiveGenes_{context_name}_Merged.csv"
-    merge_data.reset_index(drop=False, inplace=True)
-
-    split_entrez = split_gene_expression_data(merge_data)
-    split_entrez.to_csv(filepath, index_label="entrez_gene_id")
-    files_dict = {context_name: filepath.as_posix()}
+    # Count the number of sources each gene is active in
+    # set to active in final output if we meet the adjusted expression requirement
     merge_data["total_expressed"] = merge_data[expression_list].sum(axis=1)
     merge_data.loc[merge_data["total_expressed"] >= merge_data["required"], "active"] = 1
 
-    transcriptomic_details = await _get_transcriptmoic_details(merge_data)
-    transcriptomic_details_filepath = filepath.parent / f"TranscriptomicDetails_{context_name}.csv"
-    transcriptomic_details.to_csv(transcriptomic_details_filepath, index=False)
+    if force_activate_high_confidence:  # If a gene is high-confidence in at least 1 data source, set it to active
+        merge_data.loc[merge_data[high_confidence_list].sum(axis=1) > 0, "active"] = 1
 
-    logger.success(f"{context_name}: Save to {filepath}\n")
+    merge_data.to_csv(output_merged_filepath, index=False)
+    transcriptomic_details = await _get_transcriptmoic_details(merge_data, taxon_id=taxon_id)
+    transcriptomic_details.to_csv(output_transcriptomic_details_filepath, index=False)
 
-    return files_dict
+    return {context_name: output_gene_activity_filepath.as_posix()}
 
 
 async def _process(
@@ -489,7 +421,7 @@ async def _process(
         )
         adjusted_expression_requirement = 1
 
-    await _new_merge_xomics(
+    await _merge_xomics(
         context_name=context_name,
         expression_requirement=adjusted_expression_requirement,
         trna_boolean_matrix=trna_boolean_matrix,
@@ -505,67 +437,166 @@ async def _process(
     )
 
 
-
-    return
-
-
-async def merge_xomics(
-    trnaseq_filepath: str | Path | None = None,
-    mrnaseq_filepath: str | Path | None = None,
-    scrnaseq_filepath: str | Path | None = None,
-    proteomics_filepath: str | Path | None = None,
-    trna_weight: float = 1,
-    mrna_weight: float = 1,
-    scrna_weight: float = 1,
-    proteomics_weight: float = 2,
+async def merge_xomics(  # noqa: C901
+    context_name: str,
+    trna_matrix_or_filepath: Path | pd.DataFrame | None,
+    mrna_matrix_or_filepath: Path | pd.DataFrame | None,
+    scrna_matrix_or_filepath: Path | pd.DataFrame | None,
+    proteomic_matrix_or_filepath: Path | pd.DataFrame | None,
+    trna_boolean_matrix_or_filepath: Path | pd.DataFrame | None,
+    mrna_boolean_matrix_or_filepath: Path | pd.DataFrame | None,
+    scrna_boolean_matrix_or_filepath: Path | pd.DataFrame | None,
+    proteomic_boolean_matrix_or_filepath: Path | pd.DataFrame | None,
+    trna_batches: dict[int, list[str]],
+    mrna_batches: dict[int, list[str]],
+    scrna_batches: dict[int, list[str]],
+    proteomic_batches: dict[int, list[str]],
+    output_merge_activity_filepath: Path,
+    output_transcriptomic_details_filepath: Path,
+    output_trna_activity_filepath: Path | None,
+    output_mrna_activity_filepath: Path | None,
+    output_scrna_activity_filepath: Path | None,
+    output_proteomic_activity_filepath: Path | None,
+    output_final_model_scores_filepath: Path,
+    output_figure_dirpath: Path | None,
+    taxon_id: int,
+    trna_weight: int = 1,
+    mrna_weight: int = 1,
+    scrna_weight: int = 1,
+    proteomic_weight: int = 2,
+    minimum_source_expression: int = 1,
     expression_requirement: int | None = None,
     adjust_method: AdjustmentMethod = AdjustmentMethod.FLAT,
-    no_high_confidence: bool = False,
-    no_na: bool = False,
+    force_activate_high_confidence: bool = False,
+    adjust_for_na: bool = False,
     merge_zfpkm_distribution: bool = False,
     keep_transcriptomics_score: bool = True,
+    weighted_z_floor: int = -6,
+    weighted_z_ceiling: int = -6,
 ):
     """Merge expression tables of multiple sources (RNA-seq, proteomics) into one."""
+    if expression_requirement < 1:
+        raise ValueError("Expression requirement must be at least 1!")
+
     if expression_requirement is None:
         expression_requirement = sum(
             test is not None
             for test in [
-                trnaseq_filepath,
-                mrnaseq_filepath,
-                scrnaseq_filepath,
-                proteomics_filepath,
+                trna_matrix_or_filepath,
+                mrna_matrix_or_filepath,
+                scrna_matrix_or_filepath,
+                proteomic_matrix_or_filepath,
             ]
         )
-    elif expression_requirement < 1:
-        raise ValueError("Expression requirement must be at least 1!")
 
-    trnaseq_filepath = Path(trnaseq_filepath) if trnaseq_filepath else None
-    mrnaseq_filepath = Path(mrnaseq_filepath) if mrnaseq_filepath else None
-    scrnaseq_filepath = Path(scrnaseq_filepath) if scrnaseq_filepath else None
-    proteomics_filepath = Path(proteomics_filepath) if proteomics_filepath else None
-
-    await _handle_context_batch(
-        trnaseq_filepath,
-        mrnaseq_filepath,
-        scrnaseq_filepath,
-        proteomics_filepath,
-        trna_weight,
-        mrna_weight,
-        scrna_weight,
-        proteomics_weight,
-        expression_requirement,
-        adjust_method,
-        no_high_confidence,
-        no_na,
-        merge_zfpkm_distribution,
-        keep_transcriptomics_score,
+    if all(
+        file is None
+        for file in [
+            trna_matrix_or_filepath,
+            mrna_matrix_or_filepath,
+            scrna_matrix_or_filepath,
+            proteomic_matrix_or_filepath,
+        ]
+    ):
+        raise ValueError("No data was passed!")
+
+    if adjust_method not in AdjustmentMethod:
+        raise ValueError(f"Adjustment method must be one of {AdjustmentMethod}; got: {adjust_method}")
+
+    output_final_model_scores_filepath.parent.mkdir(parents=True, exist_ok=True)
+    if output_merge_activity_filepath:
+        output_merge_activity_filepath.parent.mkdir(parents=True, exist_ok=True)
+    if output_transcriptomic_details_filepath:
+        output_transcriptomic_details_filepath.parent.mkdir(parents=True, exist_ok=True)
+    if output_trna_activity_filepath:
+        output_trna_activity_filepath.parent.mkdir(parents=True, exist_ok=True)
+    if output_mrna_activity_filepath:
+        output_mrna_activity_filepath.parent.mkdir(parents=True, exist_ok=True)
+    if output_scrna_activity_filepath:
+        output_scrna_activity_filepath.parent.mkdir(parents=True, exist_ok=True)
+    if output_proteomic_activity_filepath:
+        output_proteomic_activity_filepath.parent.mkdir(parents=True, exist_ok=True)
+    if output_figure_dirpath:
+        output_figure_dirpath.mkdir(parents=True, exist_ok=True)
+
+    # fmt: off
+    trna_matrix: pd.DataFrame | None = (
+        pd.read_csv(trna_matrix_or_filepath) if isinstance(trna_matrix_or_filepath, Path)
+        else trna_matrix_or_filepath if isinstance(trna_matrix_or_filepath, pd.DataFrame)
+        else None
     )
+    mrna_matrix: pd.DataFrame | None = (
+        pd.read_csv(mrna_matrix_or_filepath) if isinstance(mrna_matrix_or_filepath, Path)
+        else mrna_matrix_or_filepath if isinstance(mrna_matrix_or_filepath, pd.DataFrame)
+        else None
     )
+    scrna_matrix: pd.DataFrame | None = (
+        pd.read_csv(scrna_matrix_or_filepath) if isinstance(scrna_matrix_or_filepath, Path)
+        else scrna_matrix_or_filepath if isinstance(scrna_matrix_or_filepath, pd.DataFrame)
+        else None
     )
+    proteomic_matrix: pd.DataFrame | None = (
+        pd.read_csv(proteomic_matrix_or_filepath) if isinstance(proteomic_matrix_or_filepath, Path)
+        else proteomic_matrix_or_filepath if isinstance(proteomic_matrix_or_filepath, pd.DataFrame)
+        else None
     )
 
+    trna_boolean_matrix: pd.DataFrame | None = (
+        pd.read_csv(trna_boolean_matrix_or_filepath) if isinstance(trna_boolean_matrix_or_filepath, Path)
+        else trna_boolean_matrix_or_filepath if isinstance(trna_boolean_matrix_or_filepath, pd.DataFrame)
+        else None
     )
+    mrna_boolean_matrix: pd.DataFrame | None = (
+        pd.read_csv(mrna_boolean_matrix_or_filepath) if isinstance(mrna_boolean_matrix_or_filepath, Path)
+        else mrna_boolean_matrix_or_filepath if isinstance(mrna_boolean_matrix_or_filepath, pd.DataFrame)
+        else None
     )
+    scrna_boolean_matrix: pd.DataFrame | None = (
+        pd.read_csv(scrna_boolean_matrix_or_filepath) if isinstance(scrna_boolean_matrix_or_filepath, Path)
+        else scrna_boolean_matrix_or_filepath if isinstance(scrna_boolean_matrix_or_filepath, pd.DataFrame)
+        else None
     )
+    proteomic_boolean_matrix: pd.DataFrame | None = (
+        pd.read_csv(proteomic_boolean_matrix_or_filepath) if isinstance(proteomic_boolean_matrix_or_filepath, Path)
+        else proteomic_boolean_matrix_or_filepath if isinstance(proteomic_boolean_matrix_or_filepath, pd.DataFrame)
+        else None
     )
+    # fmt: on
+
+    await _process(
+        context_name=context_name,
+        trna_matrix=trna_matrix,
+        mrna_matrix=mrna_matrix,
+        scrna_matrix=scrna_matrix,
+        proteomic_matrix=proteomic_matrix,
+        trna_boolean_matrix=trna_boolean_matrix,
+        mrna_boolean_matrix=mrna_boolean_matrix,
+        scrna_boolean_matrix=scrna_boolean_matrix,
+        proteomic_boolean_matrix=proteomic_boolean_matrix,
+        trna_batches=trna_batches,
+        mrna_batches=mrna_batches,
+        scrna_batches=scrna_batches,
+        proteomic_batches=proteomic_batches,
+        trna_weight=trna_weight,
+        mrna_weight=mrna_weight,
+        scrna_weight=scrna_weight,
+        proteomic_weight=proteomic_weight,
+        taxon_id=taxon_id,
+        minimum_source_expression=minimum_source_expression,
+        expression_requirement=expression_requirement,
+        weighted_z_floor=weighted_z_floor,
+        weighted_z_ceiling=weighted_z_ceiling,
+        adjust_method=adjust_method,
+        merge_zfpkm_distribution=merge_zfpkm_distribution,
+        keep_gene_score=keep_transcriptomics_score,
+        force_activate_high_confidence=force_activate_high_confidence,
+        adjust_for_missing_sources=adjust_for_na,
+        output_merge_activity_filepath=output_merge_activity_filepath,
+        output_transcriptomic_details_filepath=output_transcriptomic_details_filepath,
+        output_trna_activity_filepath=output_trna_activity_filepath,
+        output_mrna_activity_filepath=output_mrna_activity_filepath,
+        output_scrna_activity_filepath=output_scrna_activity_filepath,
+        output_proteomic_activity_filepath=output_proteomic_activity_filepath,
+        output_final_model_scores_filepath=output_final_model_scores_filepath,
+        output_figure_dirpath=output_figure_dirpath,
     )

From b5d89aa8ea3d2f393a06ad7f89b1049f720a097e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:17:52 -0600
Subject: [PATCH 104/345] refactor: import required modules

---
 main/como/combine_distributions.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index ed69413b..abcf95a9 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -1,10 +1,19 @@
+from __future__ import annotations
+
+from collections.abc import Iterator
+from dataclasses import dataclass, fields
 from pathlib import Path
+from typing import Literal, NamedTuple
 
 import numpy as np
+import numpy.typing as npt
 import pandas as pd
 import plotly.express as px
 from loguru import logger
 
+from como import graph
+from como.utils import _num_rows
+
 
 def _get_batch_name(x):
     return Path(x).stem

From bb140ab1baed19c45fecacf4ce3357e7739a2b0e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:18:18 -0600
Subject: [PATCH 105/345] feat: create understandable dataclasses

---
 main/como/combine_distributions.py | 49 ++++++++++++++++++++++++++++++
 1 file changed, 49 insertions(+)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index abcf95a9..f88a2076 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -15,6 +15,55 @@
 from como.utils import _num_rows
 
 
+class _IterableDataclass:
+    def __iter__(self) -> Iterator[str]:
+        return iter(getattr(self, field.name) for field in fields(self))  # type: ignore
+
+
+class _BatchEntry(NamedTuple):
+    batch_num: int
+    sample_names: list[str]
+
+
+@dataclass
+class _InputMatrices(_IterableDataclass):
+    trna: Path | pd.DataFrame | None
+    mrna: Path | pd.DataFrame | None
+    scrna: Path | pd.DataFrame | None
+    proteomics: Path | pd.DataFrame | None
+
+
+@dataclass
+class _BatchNames(_IterableDataclass):
+    trna: list[_BatchEntry]
+    mrna: list[_BatchEntry]
+    scrna: list[_BatchEntry]
+    proteomics: list[_BatchEntry]
+
+
+@dataclass
+class _SourceWeights(_IterableDataclass):
+    trna: int
+    mrna: int
+    scrna: int
+    proteomics: int
+
+
+@dataclass
+class _OutputCombinedSourceFilepath(_IterableDataclass):
+    trna: Path | None
+    mrna: Path | None
+    scrna: Path | None
+    proteomics: Path | None
+
+
+@dataclass
+class _CombineOmicsInput:
+    z_score_matrix: pd.DataFrame
+    type: Literal["totalrna", "mrna", "scrna", "proteomics"]
+    weight: int
+
+
 def _get_batch_name(x):
     return Path(x).stem
 

From 8102b0f2d65878ae32d8fb7096473f2080ab2e01 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:22:39 -0600
Subject: [PATCH 106/345] refactor: remove unused modules

---
 main/como/combine_distributions.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index f88a2076..77513820 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -8,8 +8,6 @@
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
-import plotly.express as px
-from loguru import logger
 
 from como import graph
 from como.utils import _num_rows

From d7caa252784734d48a394c747cd58626c52e2dc5 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:23:41 -0600
Subject: [PATCH 107/345] refactor: remove multi-context-specific code

---
 main/como/combine_distributions.py | 83 ------------------------------
 1 file changed, 83 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 77513820..78fd7ac3 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -62,89 +62,6 @@ class _CombineOmicsInput:
     weight: int
 
 
-def _get_batch_name(x):
-    return Path(x).stem
-
-
-def _parse_contexts_zfpkm(wd, contexts, prep):
-    batches = {}
-    for context in contexts:
-        files = (wd / context / prep).glob(f"rnaseq_{prep}_{context}.csv")
-        batches[context] = [_get_batch_name(f) for f in files]
-    return batches
-
-
-def _parse_contexts_zumi(wd, contexts, prep):
-    batches = {}
-    for context in contexts:
-        files = (wd / context / prep).glob(f"*{prep}*.csv")
-        batches[context] = [_get_batch_name(f) for f in files]
-    return batches
-
-
-def _parse_contexts_zscore_prot(wd, contexts):
-    batches = {}
-    for context in contexts:
-        files = (wd / context / "proteomics").glob("protein_zscore_Matrix_*.csv")
-        batches[context] = [_get_batch_name(f) for f in files]
-    return batches
-
-
-def _merge_batch(wd, context, batch):
-    files = list(wd.glob(f"*{batch}*"))
-    nrep = []
-    if not files:
-        raise ValueError(f"No files found for {context} - {batch}")
-
-    for f in files:
-        zmat = pd.read_csv(f)
-        zmat.columns = pd.Index([c.lower() for c in zmat.columns])
-        zmat["entrez_gene_id"] = zmat["entrez_gene_id"].astype(str).str.split("//").str[0]
-
-        zmat = zmat.astype({col: float for col in zmat.columns if col != "entrez_gene_id"})
-        zmat = zmat.astype({"entrez_gene_id": str})
-        zmat = zmat.groupby("entrez_gene_id").max().reset_index()
-        zmat = zmat.dropna()
-
-        nrep.append(zmat.shape[1] - 1)
-        entrez_gene = zmat["entrez_gene_id"]
-        rep_names = zmat.columns
-        zmat = pd.concat([zmat[col] for col in zmat.columns[1:]], axis=1)
-        zmat = pd.concat([entrez_gene, zmat], axis=1)
-        zmat = zmat.dropna()
-        zmat.columns = rep_names
-
-        stack_df = pd.concat(
-            [
-                pd.DataFrame(
-                    {"entrez_gene_id": zmat["entrez_gene_id"], "zscore": zmat[col].astype(float), "source": col}
-                )
-                for col in zmat.columns[1:]
-            ]
-        )
-
-        plot_name_png = wd / "figures" / f"plot_{context}_{Path(f).stem}.png"
-
-        fig = px.histogram(
-            stack_df,
-            x="zscore",
-            color="source",
-            nbins=100,  # Adjust as needed
-            marginal="rug",
-            title=f"Z-score Distribution for {context} - {Path(f).stem}",
-        )
-
-        fig.update_layout(xaxis_title="Z-score", yaxis_title="Frequency", font={"family": "sans-serif", "size": 12})
-
-        # Simplified plot for many sources (optional)
-        if len(stack_df["source"].unique()) > 10:
-            fig.update_layout(showlegend=False)
-
-        plot_name_png.parent.mkdir(parents=True, exist_ok=True)
-        fig.write_image(plot_name_png)
-
-    return zmat, nrep
-
 
 def _combine_batch_zdistro(wd, context, batch, zmat):
     plot_name_png = wd / "figures" / f"plot_{context}_{batch}_combine_distro.png"

From 672ae382e452e50d4e78e5e971dcf15e18f5ed3d Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:24:13 -0600
Subject: [PATCH 108/345] feat: do not use hardcoded paths

---
 main/como/combine_distributions.py | 569 ++++++++++++-----------------
 1 file changed, 231 insertions(+), 338 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 78fd7ac3..0943b38c 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -62,375 +62,268 @@ class _CombineOmicsInput:
     weight: int
 
 
-
-def _combine_batch_zdistro(wd, context, batch, zmat):
-    plot_name_png = wd / "figures" / f"plot_{context}_{batch}_combine_distro.png"
-
-    def weighted_z(x):
-        floor_score = -6
-        ceil_score = 6
-        x = np.array(x, dtype=float)
+def _combine_batch_zdistro(
+    matrix: pd.DataFrame,
+    context_name: str,
+    batch_num: int,
+    output_png_filepath: Path,
+    weighted_z_floor: int = -6,
+    weighted_z_ceiling: int = 6,
+) -> pd.DataFrame:
+    def weighted_z(x: npt.NDArray[float], floor: int, ceiling: int) -> npt.NDArray[float]:
         result = np.sum(x) / np.sqrt(len(x))
-        return np.clip(result, floor_score, ceil_score)
-
-    combine_z = zmat
-    if zmat.shape[1] > 2:
-        combine_z = np.apply_along_axis(weighted_z, axis=1, arr=zmat.iloc[:, 1:].values)
-        merge_df = pd.concat([zmat, pd.Series(combine_z, name="combined")], axis=1)
-        combine_z = pd.DataFrame({"entrez_gene_id": zmat["entrez_gene_id"].astype(str), "combine_z": combine_z})
-
-        stack_df = pd.concat(
-            [
-                pd.DataFrame(
-                    {"entrez_gene_id": merge_df["entrez_gene_id"], "zscore": merge_df[col].astype(float), "source": col}
-                )
-                for col in merge_df.columns[1:]
-            ]
-        )
+        return np.clip(result, floor, ceiling)
 
-        # Simplified plot for many sources (optional)
-        if len(stack_df["source"].unique()) > 10:
-            stack_df = stack_df[stack_df["source"] == "combined"]
-
-        fig = px.histogram(
-            stack_df,
-            x="zscore",
-            color="source",
-            nbins=100,
-            marginal="rug",
-            title=f"Combined Z-score Distribution for {context} - {batch}",
-        )
-        fig.update_layout(xaxis_title="Z-score", yaxis_title="Frequency", font={"family": "sans-serif", "size": 12})
-        fig.write_image(plot_name_png)
+    if _num_rows(matrix) < 2:
+        return matrix
+
+    weighted_matrix = np.apply_along_axis(
+        weighted_z,
+        axis=1,
+        arr=matrix.iloc[:, 1:].values,
+        floor=weighted_z_floor,
+        ceiling=weighted_z_ceiling,
+    )
+    merge_df = pd.concat([matrix, pd.Series(weighted_matrix, name="combined")], axis=1)
+    weighted_matrix = pd.DataFrame(
+        {
+            "ensembl_gene_id": matrix["ensembl_gene_id"].astype(str),
+            "combine_z": weighted_matrix,
+        },
+    )
 
-    return combine_z
+    stack_df = pd.concat(
+        [
+            pd.DataFrame(
+                {"ensembl_gene_id": merge_df["ensembl_gene_id"], "zscore": merge_df[col].astype(float), "source": col}
+            )
+            for col in merge_df.columns[1:]
+        ]
+    )
+    if len(stack_df["source"].unique()) > 10:
+        stack_df = stack_df[stack_df["source"] == "combined"]
 
+    graph.z_score_distribution(
+        stack_df,
+        title=f"Combined Z-score Distribution for {context_name} - batch #{batch_num}",
+        output_png_filepath=output_png_filepath,
+    )
+    return weighted_matrix
 
-def _combine_context_zdistro(wd, context, n_reps, zmat):
-    plot_name_png = wd / "figures" / f"plot_{context}_combine_batches_distro.png"
 
-    def weighted_z(x, n_reps):
-        floor_score = -6
-        ceil_score = 6
-        x = np.array(x, dtype=float)
-        nas = np.where(np.isnan(x))[0]
-        if len(nas) > 0:
-            x = np.delete(x, nas)
-            n_reps = np.delete(n_reps, nas)
+def _combine_context_zdistro(
+    matrix: pd.DataFrame,
+    context_name: str,
+    batch_num: int,
+    num_replicates: int,
+    output_png_filepath: Path,
+    weighted_z_floor: int = -6,
+    weighted_z_ceiling: int = 6,
+):
+    def weighted_z(
+        x: npt.NDArray[float],
+        n_reps: int,
+        floor: int,
+        ceiling: int,
+    ) -> npt.NDArray[float]:
+        na_values = np.where(np.isnan(x))[0]
+        if len(na_values) > 0:
+            x = np.delete(x, na_values)
+            n_reps = np.delete(n_reps, na_values)
         weights = n_reps / np.sum(n_reps)
-        numer = np.sum(weights * x)
-        denom = np.sqrt(np.sum(weights**2))
-        result = numer / denom
-        return np.clip(result, floor_score, ceil_score)
-
-    if zmat.shape[1] > 2:
-        combine_z = np.apply_along_axis(weighted_z, axis=1, arr=zmat.iloc[:, 1:].values, n_reps=n_reps)
-        merge_df = pd.concat([zmat, pd.Series(combine_z, name="combined")], axis=1)
-        combine_z = pd.DataFrame({"entrez_gene_id": zmat["entrez_gene_id"].astype(str), "combine_z": combine_z})
-
-        stack_df = pd.concat(
-            [
-                pd.DataFrame(
-                    {"entrez_gene_id": merge_df["entrez_gene_id"], "zscore": merge_df[col].astype(float), "source": col}
-                )
+        numerator = np.sum(weights * x)
+        denominator = np.sqrt(np.sum(weights**2))
+        result = numerator / denominator
+        return np.clip(result, floor, ceiling)
+
+    if _num_rows(matrix) < 2:
+        matrix.columns = ["entrez_gene_id", "combine_z"]
+        return matrix
+
+    weighted_matrix = np.apply_along_axis(
+        weighted_z,
+        axis=1,
+        arr=matrix.iloc[:, 1:].values,
+        n_reps=num_replicates,
+        floor=weighted_z_floor,
+        ceiling=weighted_z_ceiling,
+    )
+    merge_df = pd.concat([matrix, pd.Series(weighted_matrix, name="combined")], axis=1)
+    weighted_matrix = pd.DataFrame(
+        {"ensembl_gene_id": matrix["ensembl_gene_id"].astype(str), "combine_z": weighted_matrix}
+    )
+    stack_df = pd.concat(
+        [
+            pd.DataFrame(
+                {"ensembl_gene_id": merge_df["ensembl_gene_id"], "zscore": merge_df[col].astype(float), "source": col}
                 for col in merge_df.columns[1:]
-            ]
-        )
-
-        fig = px.histogram(
-            stack_df,
-            x="zscore",
-            color="source",
-            nbins=100,  # Adjust as needed
-            marginal="rug",
-            title=f"Combined Batches Z-score Distribution for {context}",
-        )
-
-        fig.update_layout(xaxis_title="Z-score", yaxis_title="Frequency", font=dict(family="sans-serif", size=12))  # noqa: C408
-
-        fig.write_image(plot_name_png)
-
-    else:
-        combine_z = zmat
-        combine_z.columns = ["entrez_gene_id", "combine_z"]
-
-    return combine_z
+            )
+        ]
+    )
+    graph.z_score_distribution(
+        df=stack_df,
+        title=f"Combined Z-score Distribution for {context_name} - batch #{batch_num}",
+        output_png_filepath=output_png_filepath,
+    )
+    return weighted_matrix
 
 
 def _combine_omics_zdistros(
-    wd,
-    context,
-    comb_batches_z_trna,
-    comb_batches_z_mrna,
-    comb_batches_z_scrna,
-    comb_batches_z_prot,
-    trna_weight: int,
-    mrna_weight: int,
-    scrna_weight: int,
-    proteomics_weight: int,
-    keep_gene_scores=True,
+    context: str,
+    zscore_results: list[_CombineOmicsInput],
+    output_png_filepath: Path,
+    weighted_z_floor: int = -6,
+    weighted_z_ceiling: int = 6,
 ):
-    fig_path = wd / context / "figures"
-    if not fig_path.exists():
-        fig_path.mkdir(parents=True)
-    plot_name_png = fig_path / f"plot_{context}_combine_omics_distro.png"
-
-    weights = []
-    names = []
-    dfs = []
-    counter = 0
-    if comb_batches_z_trna is not None:
-        counter += 1
-        weights.append(trna_weight)
-        names.append("total")
-        dfs.append(comb_batches_z_trna)
-    if comb_batches_z_mrna is not None:
-        counter += 1
-        weights.append(mrna_weight)
-        names.append("polyA")
-        dfs.append(comb_batches_z_mrna)
-    if comb_batches_z_scrna is not None:
-        counter += 1
-        weights.append(scrna_weight)
-        names.append("singleCell")
-        dfs.append(comb_batches_z_scrna)
-    if comb_batches_z_prot is not None:
-        counter += 1
-        weights.append(proteomics_weight)
-        names.append("proteome")
-        dfs.append(comb_batches_z_prot)
-
-    def weighted_z(x, weights):
-        floor_score = -6
-        ceil_score = 10
-        x = np.array(x, dtype=float)
-        nas = np.where(np.isnan(x))[0]
-        if len(nas) > 0:
-            x = np.delete(x, nas)
-            weights = np.delete(weights, nas)
+    def weighted_z(
+        x: npt.NDArray[float],
+        weights,
+        floor: int,
+        ceiling: int,
+    ):
+        na_values = np.where(np.isnan(x))[0]
+        if len(na_values) > 0:
+            x = np.delete(x, na_values)
+            weights = np.delete(weights, na_values)
         weights = weights / np.sum(weights)
-        numer = np.sum(weights * x)
-        denom = np.sqrt(np.sum(weights**2))
-        result = numer / denom
-        return np.clip(result, floor_score, ceil_score)
-
-    zmat = dfs[0].copy()
-    zmat.columns = ["entrez_gene_id", names[0]]
-    for i in range(1, counter):
-        add_df = dfs[i]
-        add_df.columns = ["entrez_gene_id", names[i]]
-        zmat = pd.merge(zmat, add_df, on="entrez_gene_id", how="outer")
-
-    if zmat.shape[1] > 2:
-        combine_z = np.apply_along_axis(weighted_z, axis=1, arr=zmat.iloc[:, 1:].values, weights=weights)
-    else:
-        combine_z = zmat.iloc[:, 1:].values
-
-    combine_z = combine_z.ravel()
-    merge_df = pd.concat([zmat, pd.Series(combine_z, name="combined")], axis=1)
-    combine_z = pd.DataFrame({"entrez_gene_id": zmat["entrez_gene_id"].astype(str), "combine_z": combine_z})
+        numerator = np.sum(weights * x)
+        denominator = np.sqrt(np.sum(weights**2))
+        result = numerator / denominator
+        return np.clip(result, floor, ceiling)
+
+    z_matrix = pd.DataFrame()
+    for result in zscore_results:
+        result.z_score_matrix.columns = ["ensembl_gene_id", result.type]
+        z_matrix = (
+            result.z_score_matrix
+            if z_matrix.empty
+            else pd.merge(z_matrix, result.z_score_matrix, on="ensembl_gene_id", how="outer")
+        )
+
+    combined_z_matrix = (
+        np.apply_along_axis(
+            weighted_z,
+            axis=1,
+            arr=z_matrix.iloc[:, 1:].values,
+            weights=[r.weight for r in zscore_results],
+            floor=weighted_z_floor,
+            ceiling=weighted_z_ceiling,
+        )
+        if _num_rows(z_matrix) > 2
+        else z_matrix.iloc[:, 1:].values
+    ).ravel()
+    merge_df = pd.concat([z_matrix, pd.Series(combined_z_matrix, name="combined")], axis=1)
+    combined_z_matrix = pd.DataFrame(
+        {
+            "ensembl_gene_id": z_matrix["ensembl_gene_id"].astype(str),
+            "combine_z": combined_z_matrix,
+        }
+    )
 
     stack_df = pd.concat(
         [
             pd.DataFrame(
-                {"entrez_gene_id": merge_df["entrez_gene_id"], "zscore": merge_df[col].astype(float), "source": col}
+                {
+                    "ensembl_gene_id": merge_df["ensembl_gene_id"],
+                    "zscore": merge_df[col].astype(float),
+                    "source": col,
+                }
             )
             for col in merge_df.columns[1:]
         ]
     )
 
-    fig = px.histogram(
-        stack_df,
-        x="zscore",
-        color="source",
-        nbins=100,  # Adjust as needed
-        marginal="rug",
+    graph.z_score_distribution(
+        df=stack_df,
         title=f"Combined Omics Z-score Distribution for {context}",
+        output_png_filepath=output_png_filepath,
     )
-
-    fig.update_layout(xaxis_title="Z-score", yaxis_title="Frequency", font={"family": "sans-serif", "size": 12})
-
-    fig.write_image(plot_name_png)
-
-    return combine_z
-
-
-def _combine_zscores(  # noqa: C901
-    working_dir,
-    context_names,
-    global_use_mrna,
-    global_use_trna,
-    global_use_scrna,
-    global_use_proteins,
-    keep_gene_scores,
-    global_trna_weight,
-    global_mrna_weight,
-    global_scrna_weight,
-    global_protein_weight,
+    return combined_z_matrix
+
+
+def _combine_zscores(
+    context_name: str,
+    input_matrices: _InputMatrices,
+    batch_names: _BatchNames,
+    source_weights: _SourceWeights,
+    output_filepaths: _OutputCombinedSourceFilepath,
+    output_figure_dirpath: Path,
+    output_final_model_scores: Path,
+    weighted_z_floor: int = -6,
+    weighted_z_ceiling: int = 6,
 ):
-    working_dir = Path(working_dir)
-    figure_output_dir = working_dir / "figures"
-    figure_output_dir.mkdir(parents=True, exist_ok=True)
-
-    global_trna_batches = _parse_contexts_zfpkm(working_dir, context_names, "total")
-    global_mrna_batches = _parse_contexts_zfpkm(working_dir, context_names, "mrna")
-    global_scrna_batches = _parse_contexts_zumi(working_dir, context_names, "scrna")
-    global_protein_batches = _parse_contexts_zscore_prot(working_dir, context_names)
-
-    for context in context_names:
-        context_use_trna = global_use_trna
-        context_use_mrna = global_use_mrna
-        context_use_scrna = global_use_scrna
-        context_use_proteins = global_use_proteins
-
-        context_trna_batch = global_trna_batches.get(context, [])
-        if len(context_trna_batch) == 0 and global_use_trna:
-            context_use_trna = False
-            logger.warning(f"No total RNA-seq zFPKM Matrix files found for {context}. Will not use for this context.")
-
-        context_mrna_batch = global_mrna_batches.get(context, [])
-        if len(context_mrna_batch) == 0 and global_use_mrna:
-            context_use_mrna = False
-            logger.warning(f"No polyA RNA-seq zFPKM Matrix files found for {context}. Will not use for this context.")
-
-        context_scrna_batch = global_scrna_batches.get(context, [])
-        if len(context_scrna_batch) == 0 and global_use_scrna:
-            context_use_scrna = False
-            logger.warning(f"No SC RNA-seq zFPKM Matrix files found for {context}. Will not use for this context.")
-
-        context_protein_batch = global_protein_batches.get(context, [])
-        if len(context_protein_batch) == 0 and global_use_proteins:
-            context_use_proteins = False
-            logger.warning(f"No proteomics z-score Matrix files found for {context}. Will not use for this context.")
-
-        comb_batches_z_trna = None
-        if context_use_trna:
-            logger.info("Will merge total RNA-seq distributions")
-            trna_workdir = working_dir / context / "total"
-            num_reps = []
-            merge_z_data = pd.DataFrame()
-
-            for batch in context_trna_batch:
-                res = _merge_batch(trna_workdir, context, batch)
-                z_matrix = res[0]
-                num_reps.extend(res[1])
-                combine_z_matrix = _combine_batch_zdistro(trna_workdir, context, batch, z_matrix)
-                combine_z_matrix.columns = ["entrez_gene_id", batch]
-                merge_z_data = (
-                    combine_z_matrix
-                    if merge_z_data.empty
-                    else pd.merge(merge_z_data, combine_z_matrix, on="entrez_gene_id", how="outer")
-                )
-
-            comb_batches_z_trna = _combine_context_zdistro(trna_workdir, context, num_reps, merge_z_data)
-            filename = trna_workdir / f"combined_zFPKM_{context}.csv"
-            comb_batches_z_trna.to_csv(filename, index=False)
-
-            if not context_use_proteins and not context_use_mrna and not context_use_scrna:
-                filename = trna_workdir / f"model_scores_{context}.csv"
-                comb_batches_z_trna.to_csv(filename, index=False)
-
-        comb_batches_z_mrna = None
-        if context_use_mrna:
-            logger.info("Will merge polyA enriched RNA-seq distributions")
-            mrna_workdir = working_dir / context / "mrna"
-            num_reps = []
+    output_figure_dirpath.mkdir(parents=True, exist_ok=True)
+    source_name: list[str] = ["totalrna", "mrna", "scrna", "proteomics"]
+    zscore_results: list[_CombineOmicsInput] = []
+    for matrix, source in zip(input_matrices, source_name):
+        matrix: pd.DataFrame | None = pd.read_csv(matrix) if isinstance(matrix, Path) else matrix
+
+        if matrix is not None:
+            if source == "totalrna":
+                batch_data = batch_names.trna
+                weight = source_weights.trna
+                output_filepath = output_filepaths.trna
+            elif source == "mrna":
+                batch_data = batch_names.mrna
+                weight = source_weights.mrna
+                output_filepath = output_filepaths.mrna
+            elif source == "scrna":
+                batch_data = batch_names.scrna
+                weight = source_weights.scrna
+                output_filepath = output_filepaths.scrna
+            elif source == "proteomics":
+                batch_data = batch_names.proteomics
+                weight = source_weights.proteomics
+                output_filepath = output_filepaths.proteomics
+            else:
+                raise ValueError(f"Invalid source; got '{source}', expected one of '{','.join(source_name)}'")
+
+            replicate_count: list[int] = []
             merge_z_data = pd.DataFrame()
-            for batch in context_mrna_batch:
-                res = _merge_batch(mrna_workdir, context, batch)
-                z_matrix = res[0]
-                num_reps.extend(res[1])
-                combine_z_matrix = _combine_batch_zdistro(mrna_workdir, context, batch, z_matrix)
-                combine_z_matrix.columns = ["entrez_gene_id", batch]
-                merge_z_data = (
-                    combine_z_matrix
-                    if merge_z_data.empty
-                    else pd.merge(merge_z_data, combine_z_matrix, on="entrez_gene_id", how="outer")
-                )
 
-            comb_batches_z_mrna = _combine_context_zdistro(mrna_workdir, context, num_reps, merge_z_data)
-            filename = mrna_workdir / f"combined_zFPKM_{context}.csv"
-            comb_batches_z_mrna.to_csv(filename, index=False)
-
-            if not context_use_proteins and not context_use_trna and not context_use_scrna:
-                filename = mrna_workdir / f"model_scores_{context}.csv"
-                comb_batches_z_mrna.to_csv(filename, index=False)
-
-        comb_batches_z_scrna = None
-        if context_use_scrna:
-            logger.info(f"Will merge single-cell RNA-seq distributions for {context}")
-            scrna_workdir = working_dir / context / "scrna"
-            num_reps = []
-            merge_z_data = pd.DataFrame()
-            for batch in context_scrna_batch:
-                res = _merge_batch(scrna_workdir, context, batch)
-                z_matrix = res[0]
-                num_reps.extend(res[1])
-                combine_z_matrix = _combine_batch_zdistro(scrna_workdir, context, batch, z_matrix)
-                combine_z_matrix.columns = ["entrez_gene_id", batch]
-                merge_z_data = (
-                    combine_z_matrix
-                    if merge_z_data.empty
-                    else pd.merge(merge_z_data, combine_z_matrix, on="entrez_gene_id", how="outer")
+            batch: _BatchEntry
+            for batch in batch_data:
+                replicate_count.append(len(batch.sample_names))
+
+                batch_df: pd.DataFrame = matrix[["ensembl_gene_id", *batch.sample_names]]
+                # graph.z_score_distribution(
+                #     batch_df,
+                #     title=f"Z-Score Distribution for {context_name} - batch #{batch.batch_num} - {source}",
+                #     output_png_filepath=output_figure_dirpath
+                #     / f"{source}_batch{batch.batch_num}_zscore_distribution.png",
+                # )
+                combine_z_matrix: pd.DataFrame = _combine_batch_zdistro(
+                    matrix=batch_df,
+                    context_name=context_name,
+                    batch_num=batch.batch_num,
+                    output_png_filepath=(
+                        output_figure_dirpath
+                        / f"combined_{source}_{context_name}_batch{batch.batch_num}_distribution.png"
+                    ),
+                    weighted_z_floor=weighted_z_floor,
+                    weighted_z_ceiling=weighted_z_ceiling,
                 )
-
-            comb_batches_z_scrna = _combine_context_zdistro(scrna_workdir, context, num_reps, merge_z_data)
-            filename = scrna_workdir / f"combined_zFPKM_{context}.csv"
-            comb_batches_z_scrna.to_csv(filename, index=False)
-
-            if not context_use_proteins and not context_use_trna and not context_use_mrna:
-                filename = scrna_workdir / f"model_scores_{context}.csv"
-                comb_batches_z_scrna.to_csv(filename, index=False)
-
-        comb_batches_z_prot = None
-        if context_use_proteins:
-            logger.info("Will merge protein abundance distributions")
-            protein_workdir = working_dir / context / "proteomics"
-            num_reps = []
-            merge_z_data = pd.DataFrame()
-            for batch in context_protein_batch:
-                res = _merge_batch(protein_workdir, context, batch)
-                z_matrix = res[0]
-                num_reps.extend(res[1])
-                combine_z_matrix = _combine_batch_zdistro(protein_workdir, context, batch, z_matrix)
-                combine_z_matrix.columns = ["entrez_gene_id", batch]
+                combine_z_matrix.columns = ["ensembl_gene_id", batch.batch_num]
                 merge_z_data = (
                     combine_z_matrix
                     if merge_z_data.empty
-                    else pd.merge(merge_z_data, combine_z_matrix, on="entrez_gene_id", how="outer")
+                    else pd.merge(merge_z_data, combine_z_matrix, on="ensembl_gene_id", how="outer")
                 )
-
-            comb_batches_z_prot = _combine_context_zdistro(protein_workdir, context, num_reps, merge_z_data)
-            filename = protein_workdir / f"combined_zscore_proteinAbundance_{context}.csv"
-            comb_batches_z_prot.to_csv(filename, index=False)
-
-            if not context_use_mrna and not context_use_trna and not context_use_scrna:
-                filename = protein_workdir / f"model_scores_{context}.csv"
-                comb_batches_z_prot.to_csv(filename, index=False)
-
-        if (
-            comb_batches_z_mrna is None
-            and comb_batches_z_trna is None
-            and comb_batches_z_scrna is None
-            and comb_batches_z_prot is None
-        ):
-            logger.critical(
-                f"The context '{context}' was found in the configuration file but no data was found on disk!"
+            combine_batches_zscore = _combine_context_zdistro(
+                matrix=merge_z_data,
+                context_name=context_name,
+                batch_num=batch.batch_num,
+                num_replicates=sum(replicate_count),
+                output_png_filepath=output_figure_dirpath / f"totalrna_{context_name}_combined_distribution.png",
+                weighted_z_floor=weighted_z_floor,
+                weighted_z_ceiling=weighted_z_ceiling,
             )
-            continue
-
-        comb_omics_z = _combine_omics_zdistros(
-            wd=working_dir,
-            context=context,
-            comb_batches_z_trna=comb_batches_z_trna,
-            comb_batches_z_mrna=comb_batches_z_mrna,
-            comb_batches_z_scrna=comb_batches_z_scrna,
-            comb_batches_z_prot=comb_batches_z_prot,
-            trna_weight=global_trna_weight,
-            mrna_weight=global_mrna_weight,
-            scrna_weight=global_scrna_weight,
-            proteomics_weight=global_protein_weight,
-        )
+            zscore_results.append(_CombineOmicsInput(z_score_matrix=combine_batches_zscore, type=source, weight=weight))  # type: ignore
+            combine_batches_zscore.to_csv(output_filepath, index=False)
 
-        filename = working_dir / context / f"model_scores_{context}.csv"
-        comb_omics_z.to_csv(filename, index=False)
+    combined_z_omics = _combine_omics_zdistros(
+        context=context_name,
+        zscore_results=zscore_results,
+        output_png_filepath=output_figure_dirpath / f"{context_name}_combined_omics_distribution.png",
+    )
+    combined_z_omics.to_csv(output_final_model_scores, index=False)

From f825055856b414570e8aa68769bf28f79a5cca75 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:24:42 -0600
Subject: [PATCH 109/345] fix: do not overlap with built-in filenames

---
 main/como/{types.py => data_types.py} | 24 +++---------------------
 1 file changed, 3 insertions(+), 21 deletions(-)
 rename main/como/{types.py => data_types.py} (75%)

diff --git a/main/como/types.py b/main/como/data_types.py
similarity index 75%
rename from main/como/types.py
rename to main/como/data_types.py
index 916d3a25..a544aa8d 100644
--- a/main/como/types.py
+++ b/main/como/data_types.py
@@ -1,28 +1,9 @@
 from __future__ import annotations
 
-from abc import ABC
 from enum import Enum
 from pathlib import Path
 from typing import Literal
 
-from pydantic import BaseModel, ConfigDict
-from pydantic_settings import BaseSettings, SettingsConfigDict
-
-
-class _BaseModel(BaseModel, ABC):
-    model_config = ConfigDict()
-
-
-class _BaseArguments(BaseSettings, ABC):
-    model_config = SettingsConfigDict(
-        cli_parse_args=True,
-        cli_kebab_case=True,
-        nested_model_default_partial_update=True,
-        case_sensitive=True,
-        cli_avoid_json=True,
-        cli_enforce_required=True,
-    )
-
 
 class RNAPrepMethod(Enum):
     TOTAL = "total"
@@ -71,5 +52,6 @@ def from_string(value: str) -> FilteringTechnique:
                 raise ValueError(f"Got a filtering technique of '{value}'; should be one of: {possible_values}")
 
 
-type_path = str | Path
-type_rna = Literal["total", "mrna"]
+PATH_TYPE = str | Path
+RNA_TYPE = Literal["total", "mrna"]
+LOG_LEVEL = Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]

From 83525726305f0925e0f43e4c7a17cbf23436f7d0 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:25:20 -0600
Subject: [PATCH 110/345] feat: numpy typing; count rows/columns function

---
 main/como/utils.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/main/como/utils.py b/main/como/utils.py
index a359de0f..84097b8f 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -9,6 +9,7 @@
 from typing import ClassVar
 
 import aiofiles
+import numpy.typing as npt
 import pandas as pd
 from fast_bioservices import BioDBNet, Output, Taxon
 from fast_bioservices.pipeline import (
@@ -263,3 +264,11 @@ async def convert_gene_data(values: list[str], taxon_id: int | str | Taxon) -> p
 def _listify(value):
     """Convert items into a list."""
     return [value] if not isinstance(value, list) else value
+
+
+def _num_rows(item: pd.DataFrame | npt.NDArray) -> int:
+    return item.shape[1]
+
+
+def _num_columns(item: pd.DataFrame | npt.NDArray) -> int:
+    return item.shape[0]

From 75c9753b91a017162b34864ba0bc9defe339a8db Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:25:31 -0600
Subject: [PATCH 111/345] fix: allow not setting index

---
 main/como/utils.py | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/main/como/utils.py b/main/como/utils.py
index 84097b8f..74d924cf 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -144,11 +144,16 @@ def stringlist_to_list(stringlist: str | list[str]) -> list[str]:
     return new_list
 
 
-def split_gene_expression_data(expression_data: pd.DataFrame, recon_algorithm: Algorithm | None = None):
+def split_gene_expression_data(
+    expression_data: pd.DataFrame,
+    recon_algorithm: Algorithm | None = None,
+    entrez_as_index: bool = True,
+):
     """Split the gene expression data into single-gene and multiple-gene names.
 
     :param expression_data: The gene expression data to map
     :param recon_algorithm: The recon algorithm used to generate the gene expression data
+    :param entrez_as_index: Should the 'entrez_gene_id' column be set as the index
     :return:
     """
     expression_data.columns = [c.lower() for c in expression_data.columns]
@@ -156,15 +161,15 @@ def split_gene_expression_data(expression_data: pd.DataFrame, recon_algorithm: A
         expression_data.rename(columns={"combine_z": "active"}, inplace=True)
 
     expression_data = expression_data[["entrez_gene_id", "active"]]
-    expression_data.loc[:, "entrez_gene_id"] = expression_data["entrez_gene_id"].astype(str)
-    single_gene_names = expression_data[~expression_data["entrez_gene_id"].str.contains("//")]
-    multiple_gene_names = expression_data[expression_data["entrez_gene_id"].str.contains("//")]
+    single_gene_names = expression_data[~expression_data["entrez_gene_id"].astype(str).str.contains("//")]
+    multiple_gene_names = expression_data[expression_data["entrez_gene_id"].astype(str).str.contains("//")]
     split_gene_names = multiple_gene_names.assign(
         entrez_gene_id=multiple_gene_names["entrez_gene_id"].str.split("///")
     ).explode("entrez_gene_id")
 
     gene_expressions = pd.concat([single_gene_names, split_gene_names], axis=0, ignore_index=True)
-    gene_expressions.set_index("entrez_gene_id", inplace=True)
+    if entrez_as_index:
+        gene_expressions.set_index("entrez_gene_id", inplace=True)
     return gene_expressions
 
 

From 8600054e06dfbaf6ef73a3e7ab78f9bd750ecd80 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:25:59 -0600
Subject: [PATCH 112/345] fix: use new data_types file

---
 main/como/rnaseq_preprocess.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 83eb504f..5004ff28 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -18,7 +18,7 @@
 from fast_bioservices.pipeline import ensembl_to_gene_id_and_symbol, gene_symbol_to_ensembl_and_gene_id
 from loguru import logger
 
-from como.types import RNAPrepMethod, type_path, type_rna
+from como.data_types import LOG_LEVEL, PATH_TYPE, RNA_TYPE, RNAPrepMethod
 from como.utils import _listify
 
 
@@ -257,7 +257,7 @@ async def _write_counts_matrix(
     config_df: pd.DataFrame,
     como_context_dir: Path,
     output_counts_matrix_filepath: Path,
-    rna: type_rna,
+    rna: RNA_TYPE,
 ) -> pd.DataFrame:
     """Create a counts matrix file by reading gene counts table(s)."""
     study_metrics = _organize_gene_counts_files(data_dir=como_context_dir)
@@ -508,9 +508,9 @@ async def read_counts(file: Path) -> list[str]:
 async def _create_matrix_file(
     context_name: str,
     output_config_filepath: Path,
-    como_context_dir: type_path,
+    como_context_dir: PATH_TYPE,
     output_counts_matrix_filepath: Path,
-    rna: type_rna,
+    rna: RNA_TYPE,
 ) -> None:
     config_df = await _create_config_df(context_name, como_context_dir=como_context_dir)
     await _write_counts_matrix(
@@ -536,7 +536,7 @@ async def _process(
     output_mrna_matrix_filepath: Path | None,
     cache: bool,
 ):
-    rna_types: list[tuple[type_rna, Path, Path]] = []
+    rna_types: list[tuple[RNA_TYPE, Path, Path]] = []
     if output_trna_config_filepath:
         rna_types.append(("total", output_trna_config_filepath, output_trna_matrix_filepath))
     if output_mrna_config_filepath:

From e8086f86a64b3deeee2ba4ae6e4a3fb84e92ded1 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:26:25 -0600
Subject: [PATCH 113/345] fix: function renaming; remove unused variables

---
 main/como/merge_xomics.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index fcd35653..f15654df 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -12,8 +12,8 @@
 from como.combine_distributions import (
     _BatchEntry,
     _BatchNames,
+    _combine_zscores,
     _InputMatrices,
-    _new_combine_zscores,
     _OutputCombinedSourceFilepath,
     _SourceWeights,
 )
@@ -338,7 +338,6 @@ async def _process(
     weighted_z_ceiling: int,
     adjust_method: AdjustmentMethod,
     merge_zfpkm_distribution: bool,
-    keep_gene_score: bool,
     force_activate_high_confidence: bool,
     adjust_for_missing_sources: bool,
     output_merge_activity_filepath: Path,
@@ -354,7 +353,7 @@ async def _process(
     num_sources = sum(1 for source in [trna_matrix, mrna_matrix, scrna_matrix, proteomic_matrix] if source is not None)
 
     if merge_zfpkm_distribution:
-        _new_combine_zscores(
+        _combine_zscores(
             context_name=context_name,
             input_matrices=_InputMatrices(
                 trna=trna_matrix,
@@ -382,7 +381,6 @@ async def _process(
             ),
             output_figure_dirpath=output_figure_dirpath,
             output_final_model_scores=output_final_model_scores_filepath,
-            keep_gene_scores=keep_gene_score,
             weighted_z_floor=weighted_z_floor,
             weighted_z_ceiling=weighted_z_ceiling,
         )
@@ -470,7 +468,6 @@ async def merge_xomics(  # noqa: C901
     force_activate_high_confidence: bool = False,
     adjust_for_na: bool = False,
     merge_zfpkm_distribution: bool = False,
-    keep_transcriptomics_score: bool = True,
     weighted_z_floor: int = -6,
     weighted_z_ceiling: int = -6,
 ):
@@ -588,7 +585,6 @@ async def merge_xomics(  # noqa: C901
         weighted_z_ceiling=weighted_z_ceiling,
         adjust_method=adjust_method,
         merge_zfpkm_distribution=merge_zfpkm_distribution,
-        keep_gene_score=keep_transcriptomics_score,
         force_activate_high_confidence=force_activate_high_confidence,
         adjust_for_missing_sources=adjust_for_na,
         output_merge_activity_filepath=output_merge_activity_filepath,

From 3ca7ea4d070af6eb44dcbba182232acc7e06ddf0 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:26:47 -0600
Subject: [PATCH 114/345] fix: do not require pydantic

---
 pyproject.toml |  2 --
 uv.lock        | 84 ++++++++++++++------------------------------------
 2 files changed, 23 insertions(+), 63 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index aa9fff60..ea9a3299 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -17,8 +17,6 @@ dependencies = [
     "openpyxl>=3.1.5",
     "pandas>=1.3.5",
     "plotly>=5.24.1",
-    "pydantic-settings",
-    "pydantic>=2.10.3",
     "scanpy>=1.9.8",
     "scikit-learn>=1.5.2",
     "scipy>=1.7.3",
diff --git a/uv.lock b/uv.lock
index 3a417c7a..4ccb715f 100644
--- a/uv.lock
+++ b/uv.lock
@@ -335,7 +335,7 @@ wheels = [
 [[package]]
 name = "cobamp"
 version = "0.2.2"
-source = { git = "https://github.com/JoshLoecker/cobamp?rev=update_packages#bccb9141e0cb35081cdaa4a98b25bcdaf2aa442a" }
+source = { git = "https://github.com/JoshLoecker/cobamp?rev=master#17bb8d8de6f7bdbdf5b2ff5b25da2b365a68f568" }
 dependencies = [
     { name = "boolean-py" },
     { name = "indexed" },
@@ -429,7 +429,7 @@ wheels = [
 
 [[package]]
 name = "como"
-version = "1.10.0"
+version = "1.11.1"
 source = { editable = "." }
 dependencies = [
     { name = "aiofiles" },
@@ -437,14 +437,11 @@ dependencies = [
     { name = "cobamp" },
     { name = "cobra" },
     { name = "fast-bioservices" },
-    { name = "gurobipy" },
     { name = "kaleido" },
     { name = "loguru" },
     { name = "openpyxl" },
     { name = "pandas" },
     { name = "plotly" },
-    { name = "pydantic" },
-    { name = "pydantic-settings" },
     { name = "scanpy" },
     { name = "scikit-learn" },
     { name = "scipy", version = "1.7.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
@@ -454,31 +451,34 @@ dependencies = [
 ]
 
 [package.optional-dependencies]
-interactive = [
-    { name = "ipython" },
-    { name = "jupyterlab" },
-]
-
-[package.dev-dependencies]
 dev = [
     { name = "commitizen" },
     { name = "commitlint" },
-    { name = "cz-conventional-gitmoji" },
     { name = "hypothesis" },
     { name = "pytest" },
     { name = "pytest-asyncio" },
     { name = "pytest-cov" },
     { name = "ruff" },
 ]
+gurobi = [
+    { name = "gurobipy" },
+]
+interactive = [
+    { name = "ipython" },
+    { name = "jupyterlab" },
+]
 
 [package.metadata]
 requires-dist = [
     { name = "aiofiles", specifier = ">=24.1.0" },
     { name = "aioftp", specifier = ">=0.23.1" },
-    { name = "cobamp", git = "https://github.com/JoshLoecker/cobamp?rev=update_packages" },
+    { name = "cobamp", git = "https://github.com/JoshLoecker/cobamp?rev=master" },
     { name = "cobra", specifier = ">=0.28.0" },
+    { name = "commitizen", marker = "extra == 'dev'", specifier = ">=4.1.0" },
+    { name = "commitlint", marker = "extra == 'dev'", specifier = ">=1.3.0" },
     { name = "fast-bioservices", specifier = ">=0.3.9" },
-    { name = "gurobipy", specifier = ">=11.0" },
+    { name = "gurobipy", marker = "extra == 'gurobi'", specifier = ">=10.0.0" },
+    { name = "hypothesis", marker = "extra == 'dev'", specifier = ">=6.122.1" },
     { name = "ipython", marker = "extra == 'interactive'", specifier = ">=8.0.0" },
     { name = "jupyterlab", marker = "extra == 'interactive'", specifier = ">=4.3.2" },
     { name = "kaleido", specifier = "==0.2.1" },
@@ -486,25 +486,15 @@ requires-dist = [
     { name = "openpyxl", specifier = ">=3.1.5" },
     { name = "pandas", specifier = ">=1.3.5" },
     { name = "plotly", specifier = ">=5.24.1" },
-    { name = "pydantic", specifier = ">=2.10.3" },
-    { name = "pydantic-settings", git = "https://github.com/pydantic/pydantic-settings" },
+    { name = "pytest", marker = "extra == 'dev'", specifier = ">=8.3.3" },
+    { name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.24.0" },
+    { name = "pytest-cov", marker = "extra == 'dev'", specifier = ">=6.0.0" },
+    { name = "ruff", marker = "extra == 'dev'", specifier = ">=0.8.0" },
     { name = "scanpy", specifier = ">=1.9.8" },
     { name = "scikit-learn", specifier = ">=1.5.2" },
     { name = "scipy", specifier = ">=1.7.3" },
     { name = "setuptools", specifier = "<60.0" },
-    { name = "troppo", git = "https://github.com/JoshLoecker/troppo?rev=update_dependencies" },
-]
-
-[package.metadata.requires-dev]
-dev = [
-    { name = "commitizen", specifier = ">=4.1.0" },
-    { name = "commitlint", specifier = ">=1.3.0" },
-    { name = "cz-conventional-gitmoji", specifier = ">=0.6.1" },
-    { name = "hypothesis", specifier = ">=6.122.1" },
-    { name = "pytest", specifier = ">=8.3.3" },
-    { name = "pytest-asyncio", specifier = ">=0.24.0" },
-    { name = "pytest-cov", specifier = ">=6.0.0" },
-    { name = "ruff", specifier = ">=0.8.0" },
+    { name = "troppo", git = "https://github.com/JoshLoecker/troppo?rev=master" },
 ]
 
 [[package]]
@@ -602,19 +592,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321 },
 ]
 
-[[package]]
-name = "cz-conventional-gitmoji"
-version = "0.6.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "attrs" },
-    { name = "commitizen" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/de/6e/0bd29b8df2fc2a1aca050bf92429b81b9dd91672dddf9d20e94008c80d99/cz_conventional_gitmoji-0.6.1.tar.gz", hash = "sha256:3fd4b355fa9a1ffc31192112f1807ab6c33203204c00f38a54c80643a2f407b8", size = 13755 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/fc/a3/2d9b33cb8b9898b0d3cc6147242ce9c3cee1e78739d793d1b197d8b573b8/cz_conventional_gitmoji-0.6.1-py3-none-any.whl", hash = "sha256:db8f51ea2f41389e6cc9986234524df6b4e1a3846ad738f5b946a811c3948623", size = 15151 },
-]
-
 [[package]]
 name = "debugpy"
 version = "1.8.9"
@@ -2080,15 +2057,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/33/72/f881b5e18fbb67cf2fb4ab253660de3c6899dbb2dba409d0b757e3559e3d/pydantic_core-2.27.1-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:981fb88516bd1ae8b0cbbd2034678a39dedc98752f264ac9bc5839d3923fa04c", size = 2001864 },
 ]
 
-[[package]]
-name = "pydantic-settings"
-version = "2.6.1"
-source = { git = "https://github.com/pydantic/pydantic-settings#7bcb6edba2f683c83419283f977a7d16ec4c3b64" }
-dependencies = [
-    { name = "pydantic" },
-    { name = "python-dotenv" },
-]
-
 [[package]]
 name = "pygments"
 version = "2.18.0"
@@ -2180,15 +2148,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892 },
 ]
 
-[[package]]
-name = "python-dotenv"
-version = "1.0.1"
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-
 [[package]]
 name = "python-json-logger"
 version = "2.0.7"
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+    { url = "https://files.pythonhosted.org/packages/30/fc/8cd12f189c6405a4c1cf37bd633aa740a9538c8e40497c231072d0fef5cf/ruamel.yaml.clib-0.2.12-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:a52d48f4e7bf9005e8f0a89209bf9a73f7190ddf0489eee5eb51377385f59f2a", size = 663393 },
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+    { url = "https://files.pythonhosted.org/packages/f0/ca/e4106ac7e80efbabdf4bf91d3d32fc424e41418458251712f5672eada9ce/ruamel.yaml.clib-0.2.12-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:1492a6051dab8d912fc2adeef0e8c72216b24d57bd896ea607cb90bb0c4981d3", size = 714498 },
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@@ -2531,6 +2492,7 @@ wheels = [
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+    { url = "https://files.pythonhosted.org/packages/db/5d/36619b61ffa2429eeaefaab4f3374666adf36ad8ac6330d855848d7d36fd/ruamel.yaml.clib-0.2.12-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:b82a7c94a498853aa0b272fd5bc67f29008da798d4f93a2f9f289feb8426a58d", size = 715692 },
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 ]
@@ -2962,7 +2924,7 @@ wheels = [
 [[package]]
 name = "troppo"
 version = "0.1.0"
-source = { git = "https://github.com/JoshLoecker/troppo?rev=update_dependencies#3b7302417d32e2fadaee356cc49e8c182a7dd531" }
+source = { git = "https://github.com/JoshLoecker/troppo?rev=master#048edc8ebc8026aa7b04ef481b0637743f346d43" }
 dependencies = [
     { name = "cobamp" },
     { name = "cobra" },

From cb896b90f09a1253c9ce2e90c5975e9eefae64e0 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:27:27 -0600
Subject: [PATCH 115/345] fix: use new file name

---
 main/como/rnaseq_gen.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 7a9a5041..977a28a9 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -25,9 +25,9 @@
 from scipy.signal import find_peaks
 from sklearn.neighbors import KernelDensity
 
+from como.data_types import FilteringTechnique, RNAPrepMethod
 from como.migrations import gene_info_migrations
 from como.project import Config
-from como.types import FilteringTechnique, RNAPrepMethod
 
 
 class _FilteringOptions(NamedTuple):

From d620960106d93462e9e0e8a2d73b629bae6d252a Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:29:12 -0600
Subject: [PATCH 116/345] fix: zfpkm should be performed on rows, not cols

---
 main/como/rnaseq_gen.py | 65 ++++++++++++++++++++++-------------------
 1 file changed, 35 insertions(+), 30 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 977a28a9..db3d50dd 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -272,7 +272,7 @@ def calculate_fpkm(metrics: NamedMetrics) -> NamedMetrics:
     return metrics
 
 
-def _zfpkm_calculation(col: pd.Series, kernel: KernelDensity, peak_parameters: tuple[float, float]) -> _ZFPKMResult:
+def _zfpkm_calculation(row: pd.Series, kernel: KernelDensity, peak_parameters: tuple[float, float]) -> _ZFPKMResult:
     """Log2 Transformations.
 
     Stabilize the variance in the data to make the distribution more symmetric; this is helpful for Gaussian fitting
@@ -316,12 +316,12 @@ def _zfpkm_calculation(col: pd.Series, kernel: KernelDensity, peak_parameters: t
             a threshold for calling a gene as "expressed"
             : https://doi.org/10.1186/1471-2164-14-778
     """
-    col_log2: npt.NDArray = np.log2(col + 1)
-    col_log2 = np.nan_to_num(col_log2, nan=0)
-    refit: KernelDensity = kernel.fit(col_log2.reshape(-1, 1))  # type: ignore
+    row_log2: npt.NDArray = np.log2(row + 1)
+    row_log2 = np.nan_to_num(row_log2, nan=0)
+    refit: KernelDensity = kernel.fit(row_log2.reshape(-1, 1))  # type: ignore
 
-    # kde: KernelDensity = KernelDensity(kernel="gaussian", bandwidth=bandwidth).fit(col_log2.reshape(-1, 1))
-    x_range = np.linspace(col_log2.min(), col_log2.max(), 1000)
+    # kde: KernelDensity = KernelDensity(kernel="gaussian", bandwidth=bandwidth).fit(row_log2.reshape(-1, 1))
+    x_range = np.linspace(row_log2.min(), row_log2.max(), 1000)
     density = np.exp(refit.score_samples(x_range.reshape(-1, 1)))
     peaks, _ = find_peaks(density, height=peak_parameters[0], distance=peak_parameters[1])
     peak_positions = x_range[peaks]
@@ -333,9 +333,9 @@ def _zfpkm_calculation(col: pd.Series, kernel: KernelDensity, peak_parameters: t
     if len(peaks) != 0:
         mu = peak_positions.max()
         max_fpkm = density[peaks[np.argmax(peak_positions)]]
-        u = col_log2[col_log2 > mu].mean()
+        u = row_log2[row_log2 > mu].mean()
         stddev = (u - mu) * np.sqrt(np.pi / 2)
-    zfpkm = pd.Series((col_log2 - mu) / stddev, dtype=np.float32, name=col.name)
+    zfpkm = pd.Series((row_log2 - mu) / stddev, dtype=np.float32, name=row.name)
 
     return _ZFPKMResult(zfpkm=zfpkm, density=Density(x_range, density), mu=mu, std_dev=stddev, max_fpkm=max_fpkm)
 
@@ -354,14 +354,13 @@ def zfpkm_transform(
         )
         update_every_percent /= 100
 
-    total = len(fpkm_df.columns)
-    update_per_step: int = int(np.ceil(total * update_every_percent))
-    cores = min(multiprocessing.cpu_count() - 2, total)
-    logger.debug(f"Processing {total:,} samples through zFPKM transform using {cores} cores")
+    total_rows = len(fpkm_df)
+    update_per_step: int = int(np.ceil(total_rows * update_every_percent))
+    cores = max(min(multiprocessing.cpu_count() - 2, total_rows), 1)  # Get at least 1 core and at most cpu_count() - 2
     logger.debug(
-        f"Will update every {update_per_step:,} steps as this is approximately "
-        f"{update_every_percent:.1%} of {total:,}"
+        f"zFPKM transforming {total_rows:,} gene(s) across {len(fpkm_df.columns)} sample(s) using {cores} cores"
     )
+    logger.debug(f"Will update every {update_per_step:,} steps (~{update_every_percent:.1%} of {total_rows:,})")
 
     with Pool(processes=cores) as pool:
         kernel = KernelDensity(kernel="gaussian", bandwidth=bandwidth)
@@ -369,34 +368,37 @@ def zfpkm_transform(
         partial_func = partial(_zfpkm_calculation, kernel=kernel, peak_parameters=peak_parameters)
         chunk_time = time.time()
         start_time = time.time()
+        log_padding = len(str(f"{total_rows:,}"))
 
-        log_padding = len(str(f"{total:,}"))
-        zfpkm_df = pd.DataFrame(data=0, index=fpkm_df.index, columns=fpkm_df.columns)
+        zfpkm_series: list[pd.Series | None] = [None] * total_rows
         results: dict[str, _ZFPKMResult] = {}
         result: _ZFPKMResult
         for i, result in enumerate(
             pool.imap(
                 partial_func,
-                (fpkm_df[col] for col in fpkm_df.columns),
+                (row for _, row in fpkm_df.iterrows()),
                 chunksize=chunksize,
             )
         ):
             key = str(result.zfpkm.name)
             results[key] = result
-            zfpkm_df[key] = result.zfpkm
+            zfpkm_series[i] = result.zfpkm
 
             # show updates every X% and at the end, but skip on first iteration
-            if i != 0 and (i % update_per_step == 0 or i == total):
+            if i != 0 and (i % update_per_step == 0 or i >= total_rows):
                 current_time = time.time()
                 chunk = current_time - chunk_time
                 total_time = current_time - start_time
-                formatted = f"{i:,}"
+                chunk_num = f"{i:,}"
                 logger.debug(
-                    f"Processed {formatted:>{log_padding}} of {total:,} - "
+                    f"Processed {chunk_num:>{log_padding}} of {total_rows:,} - "
                     f"chunk took {chunk:.1f} seconds - "
                     f"running for {total_time:.1f} seconds"
                 )
                 chunk_time = current_time
+
+    zfpkm_df = pd.concat(zfpkm_series, axis=1)
+
     return results, zfpkm_df
 
 
@@ -408,7 +410,7 @@ def zfpkm_plot(results, *, plot_xfloor: int = -4, subplot_titles: bool = True):
     :param plot_xfloor: Lower limit for the x-axis.
     :param subplot_titles: Whether to display facet titles (sample names).
     """
-    mega_df = pd.DataFrame(columns=["sample_name", "log2fpkm", "fpkm_density", "fitted_density_scaled"])
+    to_concat: list[pd.DataFrame | None] = [None] * len(results)
     for name, result in results.items():
         stddev = result.std_dev
         x = np.array(result.density.x)
@@ -419,15 +421,18 @@ def zfpkm_plot(results, *, plot_xfloor: int = -4, subplot_titles: bool = True):
         max_fitted = fitted.max()
         scale_fitted = fitted * (max_fpkm / max_fitted)
 
-        df = pd.DataFrame(
-            {
-                "sample_name": [name] * len(x),
-                "log2fpkm": x,
-                "fpkm_density": y,
-                "fitted_density_scaled": scale_fitted,
-            }
+        to_concat.append(
+            pd.DataFrame(
+                {
+                    "sample_name": [name] * len(x),
+                    "log2fpkm": x,
+                    "fpkm_density": y,
+                    "fitted_density_scaled": scale_fitted,
+                }
+            )
         )
-        mega_df = pd.concat([mega_df, df], ignore_index=True)
+    mega_df = pd.concat(to_concat, ignore_index=True)
+    mega_df.columns = pd.Series(data=["sample_name", "log2fpkm", "fpkm_density", "fitted_density_scaled"])
 
     mega_df = mega_df.melt(id_vars=["log2fpkm", "sample_name"], var_name="source", value_name="density")
     subplot_titles = list(results.keys()) if subplot_titles else None

From 9541824388ac63022f4b8f558de3a715a6c3e157 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:30:18 -0600
Subject: [PATCH 117/345] style: reorganize code

---
 main/como/rnaseq_gen.py | 72 +++++++++++++++++++++++++----------------
 1 file changed, 45 insertions(+), 27 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index db3d50dd..79c1984b 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -171,8 +171,10 @@ async def _build_matrix_results(
     :param taxon: The NCBI Taxon ID
     :return: A dataclass `ReadMatrixResults`
     """
-    gene_info = gene_info_migrations(gene_info)
     conversion = await ensembl_to_gene_id_and_symbol(ids=matrix["ensembl_gene_id"].tolist(), taxon=taxon)
+    conversion["ensembl_gene_id"] = conversion["ensembl_gene_id"].str.split(",")
+    conversion = conversion.explode("ensembl_gene_id")
+    conversion.reset_index(inplace=True, drop=True)
     matrix = matrix.merge(conversion, on="ensembl_gene_id", how="left")
 
     # Only include Entrez and Ensembl Gene IDs that are present in `gene_info`
@@ -181,6 +183,7 @@ async def _build_matrix_results(
     matrix = matrix.replace(to_replace="-", value=pd.NA).dropna()
     matrix["entrez_gene_id"] = matrix["entrez_gene_id"].astype(int)
 
+    gene_info = gene_info_migrations(gene_info)
     gene_info = gene_info.replace(to_replace="-", value=pd.NA).dropna()
     gene_info["entrez_gene_id"] = gene_info["entrez_gene_id"].astype(int)
 
@@ -189,6 +192,7 @@ async def _build_matrix_results(
         on=["entrez_gene_id", "ensembl_gene_id"],
         how="inner",
     )
+
     gene_info = gene_info.merge(
         counts_matrix[["entrez_gene_id", "ensembl_gene_id"]],
         on=["entrez_gene_id", "ensembl_gene_id"],
@@ -552,7 +556,7 @@ def tpm_quantile_filter(*, metrics: NamedMetrics, filtering_options: _FilteringO
 
         # Only keep `entrez_gene_ids` that pass `min_genes`
         metric.entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, min_genes) if keep]
-        metric.gene_sizes = [gene for gene, keep in zip(gene_size, min_genes) if keep]
+        metric.gene_sizes = np.array(gene for gene, keep in zip(gene_size, min_genes) if keep)
         metric.count_matrix = metric.count_matrix.iloc[min_genes, :]
         metric.normalization_matrix = metrics[sample].normalization_matrix.iloc[min_genes, :]
 
@@ -569,15 +573,13 @@ def zfpkm_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions,
     min_sample_expression = filtering_options.replicate_ratio
     high_confidence_sample_expression = filtering_options.high_replicate_ratio
     cut_off = filtering_options.cut_off
-
-    if calcualte_fpkm:
-        metrics = calculate_fpkm(metrics)
+    metrics = calculate_fpkm(metrics) if calcualte_fpkm else metrics
 
     metric: _StudyMetrics
     for metric in metrics.values():
         # if fpkm was not calculated, the normalization matrix will be empty; collect the count matrix instead
         matrix = metric.count_matrix if metric.normalization_matrix.empty else metric.normalization_matrix
-        matrix = matrix[matrix.sum(axis=1) > 0]
+        matrix = matrix[matrix.sum(axis=1) > 0]  # remove rows (genes) that have no counts
 
         minimums = matrix == 0
         results, zfpkm_df = zfpkm_transform(matrix)
@@ -628,7 +630,6 @@ async def _save_rnaseq_tests(
     rnaseq_matrix: pd.DataFrame,
     metadata_df: pd.DataFrame,
     gene_info_df: pd.DataFrame,
-    output_filepath: Path,
     prep: RNAPrepMethod,
     taxon: int,
     replicate_ratio: float,
@@ -637,6 +638,8 @@ async def _save_rnaseq_tests(
     high_batch_ratio: float,
     technique: FilteringTechnique,
     cut_off: int | float,
+    output_boolean_activity_filepath: Path,
+    output_zscore_normalization_filepath: Path,
 ):
     """Save the results of the RNA-Seq tests to a CSV file."""
     filtering_options = _FilteringOptions(
@@ -656,7 +659,7 @@ async def _save_rnaseq_tests(
     metrics = read_counts_results.metrics
     entrez_gene_ids = read_counts_results.entrez_gene_ids
 
-    metrics = filter_counts(
+    metrics: NamedMetrics = filter_counts(
         context_name=context_name,
         metrics=metrics,
         technique=technique,
@@ -664,11 +667,24 @@ async def _save_rnaseq_tests(
         prep=prep,
     )
 
+    merged_zscore_df = pd.DataFrame()
     expressed_genes: list[str] = []
     top_genes: list[str] = []
     for metric in metrics.values():
         expressed_genes.extend(metric.entrez_gene_ids)
         top_genes.extend(metric.high_confidence_entrez_gene_ids)
+        if metric.normalization_matrix is not None:
+            merged_zscore_df = (
+                metric.normalization_matrix
+                if merged_zscore_df.empty
+                else pd.concat(
+                    [merged_zscore_df, metric.normalization_matrix],
+                    axis=1,
+                )
+            )
+    merged_zscore_df.index = pd.Series(entrez_gene_ids, name="entrez_gene_id")
+    merged_zscore_df.to_csv(output_zscore_normalization_filepath, index=True)
+    logger.success(f"Wrote z-score normalization matrix to {output_zscore_normalization_filepath}")
 
     expression_frequency = pd.Series(expressed_genes).value_counts()
     expression_df = pd.DataFrame(
@@ -692,11 +708,11 @@ async def _save_rnaseq_tests(
     expressed_count = len(boolean_matrix[boolean_matrix["expressed"] == 1])
     high_confidence_count = len(boolean_matrix[boolean_matrix["high"] == 1])
 
-    boolean_matrix.to_csv(output_filepath, index=False)
+    boolean_matrix.to_csv(output_boolean_activity_filepath, index=False)
     logger.info(
         f"{context_name} - Found {expressed_count} expressed and {high_confidence_count} confidently expressed genes"
     )
-    logger.success(f"Wrote boolean matrix to {output_filepath}")
+    logger.success(f"Wrote boolean matrix to {output_boolean_activity_filepath}")
 
 
 async def _create_metadata_df(path: Path) -> pd.DataFrame:
@@ -708,15 +724,15 @@ async def _create_metadata_df(path: Path) -> pd.DataFrame:
     return pd.read_excel(path)
 
 
-async def rnaseq_gen(  # noqa: C901, allow complex function
+async def rnaseq_gen(
     context_name: str,
     input_rnaseq_filepath: Path,
     input_gene_info_filepath: Path,
-    output_rnaseq_filepath: Path,
     prep: RNAPrepMethod,
-    taxon: int,
-    input_metadata_filepath: Path | None = None,
-    input_metadata_df: pd.DataFrame | None = None,
+    taxon_id: int,
+    output_boolean_activity_filepath: Path,
+    output_zscore_normalization_filepath: Path,
+    input_metadata_filepath_or_df: Path | pd.DataFrame,
     replicate_ratio: float = 0.5,
     high_replicate_ratio: float = 1.0,
     batch_ratio: float = 0.5,
@@ -733,11 +749,11 @@ async def rnaseq_gen(  # noqa: C901, allow complex function
     :param context_name: The name of the context being processed
     :param input_rnaseq_filepath: The filepath to the gene count matrix
     :param input_gene_info_filepath: The filepath to the gene info file
-    :param output_rnaseq_filepath: The filepath to write the output gene count matrix
+    :param output_boolean_activity_filepath: The filepath to write the output gene count matrix
+    :param output_zscore_normalization_filepath: The filepath to write the output z-score normalization matrix
     :param prep: The preparation method
-    :param taxon: The NCBI Taxon ID
-    :param input_metadata_filepath: The filepath to the metadata file
-    :param input_metadata_df: The metadata dataframe
+    :param taxon_id: The NCBI Taxon ID
+    :param input_metadata_filepath_or_df: The filepath or dataframe containing metadata information
     :param replicate_ratio: The percentage of replicates that a gene must
         appear in for a gene to be marked as "active" in a batch/study
     :param batch_ratio: The percentage of batches that a gene must appear in for a gene to be marked as 'active"
@@ -749,9 +765,6 @@ async def rnaseq_gen(  # noqa: C901, allow complex function
     :param cutoff: The cutoff value to use for the provided filtering technique
     :return: None
     """
-    if not input_metadata_df and not input_metadata_filepath:
-        raise ValueError("At least one of input_metadata_filepath or input_metadata_df must be provided")
-
     technique = (
         FilteringTechnique.from_string(str(technique.lower())) if isinstance(technique, (str, int)) else technique
     )
@@ -787,20 +800,25 @@ async def rnaseq_gen(  # noqa: C901, allow complex function
         )
 
     logger.debug(f"Starting '{context_name}'")
-    output_rnaseq_filepath.parent.mkdir(parents=True, exist_ok=True)
-
+    output_boolean_activity_filepath.parent.mkdir(parents=True, exist_ok=True)
+    metadata_df = (
+        input_metadata_filepath_or_df
+        if isinstance(input_metadata_filepath_or_df, pd.DataFrame)
+        else await _create_metadata_df(input_metadata_filepath_or_df)
+    )
     await _save_rnaseq_tests(
         context_name=context_name,
         rnaseq_matrix=await _read_counts(input_rnaseq_filepath),
-        metadata_df=input_metadata_df or await _create_metadata_df(input_metadata_filepath),
+        metadata_df=metadata_df,
         gene_info_df=pd.read_csv(input_gene_info_filepath),
-        output_filepath=output_rnaseq_filepath,
         prep=prep,
-        taxon=taxon,
+        taxon=taxon_id,
         replicate_ratio=replicate_ratio,
         batch_ratio=batch_ratio,
         high_replicate_ratio=high_replicate_ratio,
         high_batch_ratio=high_batch_ratio,
         technique=technique,
         cut_off=cutoff,
+        output_boolean_activity_filepath=output_boolean_activity_filepath,
+        output_zscore_normalization_filepath=output_zscore_normalization_filepath,
     )

From 4ba45e0b9ec530c4d86f110aac7c114bc4a0e4bf Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:30:36 -0600
Subject: [PATCH 118/345] style: rename arguments

---
 main/como/rnaseq_preprocess.py | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 5004ff28..b869a97e 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -578,12 +578,12 @@ async def rnaseq_preprocess(
     output_gene_info_filepath: Path,
     como_context_dir: Path | None = None,
     input_matrix_filepath: Path | list[Path] | None = None,
-    output_trna_config_filepath: Path | None = None,
-    output_mrna_config_filepath: Path | None = None,
+    output_trna_metadata_filepath: Path | None = None,
+    output_mrna_metadata_filepath: Path | None = None,
     output_trna_count_matrix_filepath: Path | None = None,
     output_mrna_count_matrix_filepath: Path | None = None,
     cache: bool = True,
-    log_level: Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] = "INFO",
+    log_level: LOG_LEVEL = "INFO",
     log_location: str | TextIOWrapper = sys.stderr,
 ) -> None:
     """Preprocesses RNA-seq data for downstream analysis.
@@ -594,8 +594,8 @@ async def rnaseq_preprocess(
     :param context_name: The context/cell type being processed
     :param taxon: The NCBI taxonomy ID
     :param output_gene_info_filepath: Path to the output gene information CSV file
-    :param output_trna_config_filepath: Path to the output tRNA config file (if in "create" mode)
-    :param output_mrna_config_filepath: Path to the output mRNA config file (if in "create" mode)
+    :param output_trna_metadata_filepath: Path to the output tRNA config file (if in "create" mode)
+    :param output_mrna_metadata_filepath: Path to the output mRNA config file (if in "create" mode)
     :param output_trna_count_matrix_filepath: The path to write total RNA count matrices
     :param output_mrna_count_matrix_filepath: The path to write messenger RNA count matrices
     :param como_context_dir: If in "create" mode, the input path(s) to the COMO_input directory of the current context
@@ -616,11 +616,11 @@ async def rnaseq_preprocess(
     output_gene_info_filepath = output_gene_info_filepath.resolve()
     como_context_dir = como_context_dir.resolve()
     input_matrix_filepath = [i.resolve() for i in _listify(input_matrix_filepath)] if input_matrix_filepath else None
-    output_trna_config_filepath = (
-        output_trna_config_filepath.resolve() if output_trna_config_filepath else output_trna_config_filepath
+    output_trna_metadata_filepath = (
+        output_trna_metadata_filepath.resolve() if output_trna_metadata_filepath else output_trna_metadata_filepath
     )
-    output_mrna_config_filepath = (
-        output_mrna_config_filepath.resolve() if output_mrna_config_filepath else output_mrna_config_filepath
+    output_mrna_metadata_filepath = (
+        output_mrna_metadata_filepath.resolve() if output_mrna_metadata_filepath else output_mrna_metadata_filepath
     )
     output_trna_count_matrix_filepath = (
         output_trna_count_matrix_filepath.resolve()
@@ -640,8 +640,8 @@ async def rnaseq_preprocess(
         como_context_dir=como_context_dir,
         input_matrix_filepath=input_matrix_filepath,
         output_gene_info_filepath=output_gene_info_filepath,
-        output_trna_config_filepath=output_trna_config_filepath,
-        output_mrna_config_filepath=output_mrna_config_filepath,
+        output_trna_config_filepath=output_trna_metadata_filepath,
+        output_mrna_config_filepath=output_mrna_metadata_filepath,
         output_trna_matrix_filepath=output_trna_count_matrix_filepath,
         output_mrna_matrix_filepath=output_mrna_count_matrix_filepath,
         cache=cache,

From 0a0824e850480cbb89d72ad7d5ea05bb17c6b2a3 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:36:59 -0600
Subject: [PATCH 119/345] fix: ignore mismatched types

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 79c1984b..1b9399b0 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -414,7 +414,7 @@ def zfpkm_plot(results, *, plot_xfloor: int = -4, subplot_titles: bool = True):
     :param plot_xfloor: Lower limit for the x-axis.
     :param subplot_titles: Whether to display facet titles (sample names).
     """
-    to_concat: list[pd.DataFrame | None] = [None] * len(results)
+    to_concat: list[pd.DataFrame] = [None] * len(results)  # type: ignore  # ignoring because None is not of type pd.DataFrame
     for name, result in results.items():
         stddev = result.std_dev
         x = np.array(result.density.x)

From 2dbebb622c280c361cc1517b0434f839dfa8f690 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 17 Dec 2024 15:38:05 -0600
Subject: [PATCH 120/345] fix: misspelling

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 1b9399b0..b8a2f735 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -239,7 +239,7 @@ def calculate_tpm(metrics: NamedMetrics) -> NamedMetrics:
     return metrics
 
 
-def calculate_fpkm(metrics: NamedMetrics) -> NamedMetrics:
+def _calculate_fpkm(metrics: NamedMetrics) -> NamedMetrics:
     """Calculate the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) for each sample in the metrics dictionary."""  # noqa: E501
     for study in metrics:
         matrix_values = []
@@ -568,12 +568,12 @@ def tpm_quantile_filter(*, metrics: NamedMetrics, filtering_options: _FilteringO
     return metrics
 
 
-def zfpkm_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions, calcualte_fpkm: bool) -> NamedMetrics:
+def zfpkm_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions, calculate_fpkm: bool) -> NamedMetrics:
     """Apply zFPKM filtering to the FPKM matrix for a given sample."""
     min_sample_expression = filtering_options.replicate_ratio
     high_confidence_sample_expression = filtering_options.high_replicate_ratio
     cut_off = filtering_options.cut_off
-    metrics = calculate_fpkm(metrics) if calcualte_fpkm else metrics
+    metrics = _calculate_fpkm(metrics) if calculate_fpkm else metrics
 
     metric: _StudyMetrics
     for metric in metrics.values():
@@ -618,9 +618,9 @@ def filter_counts(
         case FilteringTechnique.tpm:
             return tpm_quantile_filter(metrics=metrics, filtering_options=filtering_options)
         case FilteringTechnique.zfpkm:
-            return zfpkm_filter(metrics=metrics, filtering_options=filtering_options, calcualte_fpkm=True)
+            return zfpkm_filter(metrics=metrics, filtering_options=filtering_options, calculate_fpkm=True)
         case FilteringTechnique.umi:
-            return zfpkm_filter(metrics=metrics, filtering_options=filtering_options, calcualte_fpkm=False)
+            return zfpkm_filter(metrics=metrics, filtering_options=filtering_options, calculate_fpkm=False)
         case _:
             raise ValueError(f"Technique must be one of {FilteringTechnique}")
 

From cf06da4379097445fde06c216ee98e5c01db4d5e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Wed, 25 Dec 2024 17:09:49 -0600
Subject: [PATCH 121/345] feat: move zscore graphing to its own module

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/graph/__init__.py | 35 +++++++++++++++++++++++++++++++++++
 1 file changed, 35 insertions(+)
 create mode 100644 main/como/graph/__init__.py

diff --git a/main/como/graph/__init__.py b/main/como/graph/__init__.py
new file mode 100644
index 00000000..1bb4cdb8
--- /dev/null
+++ b/main/como/graph/__init__.py
@@ -0,0 +1,35 @@
+from pathlib import Path
+from loguru import logger
+import pandas as pd
+import plotly.express as px
+
+__all__ = ["z_score_distribution"]
+
+
+def z_score_distribution(
+    df: pd.DataFrame,
+    title: str,
+    output_png_filepath: Path,
+):
+    if not output_png_filepath.suffix == ".png":
+        logger.warning(
+            f"Expected .png suffix for output_png_filepath, got {output_png_filepath.suffix}. Defaulting to .png"
+        )
+        output_png_filepath = output_png_filepath.with_suffix(".png")
+
+    fig = px.histogram(
+        df,
+        x="zscore",
+        color="source",
+        nbins=100,
+        marginal="rug",
+        title=title,
+    )
+
+    fig.update_layout(xaxis_title="Z-score", yaxis_title="Frequency", font={"family": "sans-serif", "size": 12})
+
+    # Simplified plot for many sources (optional)
+    if len(df["source"].unique()) > 10:
+        fig.update_layout(showlegend=False)
+
+    fig.write_image(output_png_filepath)

From e14af0c7901a7db21458b3b27e5762fd6b380f29 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Wed, 25 Dec 2024 17:09:59 -0600
Subject: [PATCH 122/345] fix: explicit import graphing

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/combine_distributions.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 0943b38c..595f4442 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -9,7 +9,7 @@
 import numpy.typing as npt
 import pandas as pd
 
-from como import graph
+from como.graph import z_score_distribution as graph_zscore_distribution
 from como.utils import _num_rows
 
 
@@ -103,7 +103,7 @@ def weighted_z(x: npt.NDArray[float], floor: int, ceiling: int) -> npt.NDArray[f
     if len(stack_df["source"].unique()) > 10:
         stack_df = stack_df[stack_df["source"] == "combined"]
 
-    graph.z_score_distribution(
+    graph_zscore_distribution(
         stack_df,
         title=f"Combined Z-score Distribution for {context_name} - batch #{batch_num}",
         output_png_filepath=output_png_filepath,
@@ -160,7 +160,7 @@ def weighted_z(
             )
         ]
     )
-    graph.z_score_distribution(
+    graph_zscore_distribution(
         df=stack_df,
         title=f"Combined Z-score Distribution for {context_name} - batch #{batch_num}",
         output_png_filepath=output_png_filepath,
@@ -233,7 +233,7 @@ def weighted_z(
         ]
     )
 
-    graph.z_score_distribution(
+    graph_zscore_distribution(
         df=stack_df,
         title=f"Combined Omics Z-score Distribution for {context}",
         output_png_filepath=output_png_filepath,

From a509786c93f1ba762ac35053715bd4abfdce5cbd Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Wed, 25 Dec 2024 17:10:35 -0600
Subject: [PATCH 123/345] fix: hotfix - zscore ceiling is incorrect

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/merge_xomics.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index f15654df..f58a69b3 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -469,7 +469,7 @@ async def merge_xomics(  # noqa: C901
     adjust_for_na: bool = False,
     merge_zfpkm_distribution: bool = False,
     weighted_z_floor: int = -6,
-    weighted_z_ceiling: int = -6,
+    weighted_z_ceiling: int = 6,
 ):
     """Merge expression tables of multiple sources (RNA-seq, proteomics) into one."""
     if expression_requirement < 1:

From fb384eca1c5f6371e6e5a694f45f88bf9bafa015 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 13:46:47 -0600
Subject: [PATCH 124/345] fix: pin numpy to less than version 2; update scanpy

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 pyproject.toml |   5 +-
 uv.lock        | 300 +++++++++++++++++--------------------------------
 2 files changed, 108 insertions(+), 197 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index ea9a3299..70ceb3e8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,10 +14,11 @@ dependencies = [
     "fast-bioservices>=0.3.9",
     "kaleido==0.2.1",
     "loguru>=0.7.2",
+    "numpy<2.0",
     "openpyxl>=3.1.5",
     "pandas>=1.3.5",
     "plotly>=5.24.1",
-    "scanpy>=1.9.8",
+    "scanpy>=1.10.4",
     "scikit-learn>=1.5.2",
     "scipy>=1.7.3",
     "setuptools<60.0",
@@ -58,4 +59,4 @@ name = "cz_conventional_commits"
 tag_format = "$version"
 version_scheme = "semver2"
 version_provider = "pep621"
-update_changelog_on_bump = true
\ No newline at end of file
+update_changelog_on_bump = true
diff --git a/uv.lock b/uv.lock
index 4ccb715f..7d354a68 100644
--- a/uv.lock
+++ b/uv.lock
@@ -33,12 +33,10 @@ dependencies = [
     { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
     { name = "h5py" },
     { name = "natsort" },
-    { name = "numpy", version = "1.22.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.1.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy" },
     { name = "packaging" },
     { name = "pandas" },
-    { name = "scipy", version = "1.7.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "scipy", version = "1.14.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "scipy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/c1/40/8e8c3baca1b00f107cdd2f9b376bcf97de1a7912786b7b4012343228d143/anndata-0.10.5.post1.tar.gz", hash = "sha256:9a17c6eda9fc40759b3f5f81742f5d18c1a0a1acdf02f13e1646700ec082c155", size = 163852 }
 wheels = [
@@ -340,13 +338,11 @@ dependencies = [
     { name = "boolean-py" },
     { name = "indexed" },
     { name = "matplotlib" },
-    { name = "numpy", version = "1.22.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.1.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy" },
     { name = "optlang" },
     { name = "pandas" },
     { name = "pathos" },
-    { name = "scipy", version = "1.7.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "scipy", version = "1.14.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "scipy" },
 ]
 
 [[package]]
@@ -360,8 +356,7 @@ dependencies = [
     { name = "future" },
     { name = "httpx" },
     { name = "importlib-resources" },
-    { name = "numpy", version = "1.22.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.1.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy" },
     { name = "optlang" },
     { name = "pandas" },
     { name = "pydantic" },
@@ -439,13 +434,13 @@ dependencies = [
     { name = "fast-bioservices" },
     { name = "kaleido" },
     { name = "loguru" },
+    { name = "numpy" },
     { name = "openpyxl" },
     { name = "pandas" },
     { name = "plotly" },
     { name = "scanpy" },
     { name = "scikit-learn" },
-    { name = "scipy", version = "1.7.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "scipy", version = "1.14.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "scipy" },
     { name = "setuptools" },
     { name = "troppo" },
 ]
@@ -483,6 +478,7 @@ requires-dist = [
     { name = "jupyterlab", marker = "extra == 'interactive'", specifier = ">=4.3.2" },
     { name = "kaleido", specifier = "==0.2.1" },
     { name = "loguru", specifier = ">=0.7.2" },
+    { name = "numpy", specifier = "<2.0" },
     { name = "openpyxl", specifier = ">=3.1.5" },
     { name = "pandas", specifier = ">=1.3.5" },
     { name = "plotly", specifier = ">=5.24.1" },
@@ -490,7 +486,7 @@ requires-dist = [
     { name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.24.0" },
     { name = "pytest-cov", marker = "extra == 'dev'", specifier = ">=6.0.0" },
     { name = "ruff", marker = "extra == 'dev'", specifier = ">=0.8.0" },
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-]
-
 [[package]]
 name = "scipy"
 version = "1.14.1"
 source = { registry = "https://pypi.org/simple" }
-resolution-markers = [
-    "python_full_version == '3.11.*'",
-    "python_full_version >= '3.12'",
-]
 dependencies = [
-    { name = "numpy", version = "2.1.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/62/11/4d44a1f274e002784e4dbdb81e0ea96d2de2d1045b2132d5af62cc31fd28/scipy-1.14.1.tar.gz", hash = "sha256:5a275584e726026a5699459aa72f828a610821006228e841b94275c4a7c08417", size = 58620554 }
 wheels = [
@@ -2650,8 +2566,7 @@ version = "0.13.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "matplotlib" },
-    { name = "numpy", version = "1.22.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.1.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy" },
     { name = "pandas" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/86/59/a451d7420a77ab0b98f7affa3a1d78a313d2f7281a57afb1a34bae8ab412/seaborn-0.13.2.tar.gz", hash = "sha256:93e60a40988f4d65e9f4885df477e2fdaff6b73a9ded434c1ab356dd57eefff7", size = 1457696 }
@@ -2741,13 +2656,11 @@ name = "statsmodels"
 version = "0.13.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", version = "1.22.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.1.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy" },
     { name = "packaging" },
     { name = "pandas" },
     { name = "patsy" },
-    { name = "scipy", version = "1.7.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "scipy", version = "1.14.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "scipy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/e1/4a/0eb4efa49cc352e2721e2aebfe8573264db2add195545ec3979c98040c3b/statsmodels-0.13.2.tar.gz", hash = "sha256:77dc292c9939c036a476f1770f9d08976b05437daa229928da73231147cde7d4", size = 17874260 }
 wheels = [
@@ -2905,7 +2818,7 @@ name = "tqdm"
 version = "4.66.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "platform_system == 'Windows'" },
+    { name = "colorama", marker = "sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/e9/34/bef135b27fe1864993a5284ad001157ee9b5538e859ac90f5b0e8cc8c9ec/tqdm-4.66.6.tar.gz", hash = "sha256:4bdd694238bef1485ce839d67967ab50af8f9272aab687c0d7702a01da0be090", size = 169533 }
 wheels = [
@@ -2963,14 +2876,11 @@ name = "umap-learn"
 version = "0.5.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numba", version = "0.53.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
-    { name = "numba", version = "0.58.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "1.22.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.1.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numba" },
+    { name = "numpy" },
     { name = "pynndescent" },
     { name = "scikit-learn" },
-    { name = "scipy", version = "1.7.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "scipy", version = "1.14.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "scipy" },
     { name = "tqdm" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/6f/d4/9ed627905f7993349671283b3c5bf2d9f543ef79229fa1c7e01324eb900c/umap-learn-0.5.7.tar.gz", hash = "sha256:b2a97973e4c6ffcebf241100a8de589a4c84126a832ab40f296c6d9fcc5eb19e", size = 92680 }

From e659eb28a4c2da7e3dedac307acb1e0e63b4503c Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 13:52:21 -0600
Subject: [PATCH 125/345] refactor!: moved `return_placeholder_data` to
 `como.utils`

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/COMO.ipynb       | 262 +++++++++++++++++++++++++++++++-----------
 main/como/__init__.py |  11 +-
 main/como/utils.py    |   3 +
 3 files changed, 198 insertions(+), 78 deletions(-)

diff --git a/main/COMO.ipynb b/main/COMO.ipynb
index 3cbebec1..8d79c4be 100644
--- a/main/COMO.ipynb
+++ b/main/COMO.ipynb
@@ -232,34 +232,42 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-16T20:09:29.761117Z",
+     "start_time": "2024-12-16T20:09:29.754605Z"
+    }
+   },
    "source": [
     "from pathlib import Path\n",
     "\n",
     "from como.rnaseq_preprocess import rnaseq_preprocess\n",
-    "from como.types import RNAPrepMethod\n",
+    "from como.data_types import RNAType\n",
     "\n",
     "taxon_id = 9606\n",
     "context_names = [\"naiveB\"]\n",
     "\n",
-    "gene_info_filepath = {context: Path(f\"data/results/{context}/gene_info.csv\") for context in context_names}\n",
+    "# Input Components\n",
     "como_context_dir = {context: Path(f\"data/COMO_input/{context}\") for context in context_names}\n",
+    "gene_info_filepath = {context: Path(f\"data/results/{context}/gene_info.csv\") for context in context_names}\n",
     "trna_matrix_filepath = {context: Path(f\"data/results/{context}/total-rna/totalrna_{context}.csv\") for context in context_names}\n",
-    "mrna_matrix_filepath = {context: Path(f\"data/results/{context}/polya-rna/polyarna_{context}.csv\") for context in context_names}\n",
+    "mrna_matrix_filepath = {context: Path(f\"data/results/{context}/mrna/mrna_{context}.csv\") for context in context_names}\n",
     "proteomics_matrix_filepath = {context: Path(f\"data/data_matrices/{context}/protein_abundance_{context}.csv\") for context in context_names}\n",
     "\n",
+    "# No single-cell data is provided by default; COMO accepts single-cell data in CSV or h5ad format\n",
+    "# If you are using single-cell data, adjust the following lines accordingly\n",
+    "scrna_matrix_filepath = {context: Path(f\"data/results/{context}/scrna/scrna_{context}.csv\") for context in context_names}\n",
+    "# scrna_matrix_filepath = [Path(f\"data/results/{context}/scrna/scrna_{context}.h5ad\") for context in context_names]\n",
+    "\n",
     "trna_metadata_filepath = Path(\"data/config_sheets/trna_config.xlsx\")\n",
     "mrna_metadata_filepath = Path(\"data/config_sheets/mrna_config.xlsx\")\n",
     "proteomics_metadata_filepath = Path(\"data/config_sheets/proteomics_config.xlsx\")\n",
     "\n",
-    "# No single-cell data is provided by default; COMO accepts single-cell data in CSV or h5ad format\n",
-    "# If you are using single-cell data, adjust the following lines accordingly\n",
-    "scrna_matrix_filepath = {context: Path(f\"data/results/{context}/scrna/scrna_{context}.csv\") for context in context_names}\n",
-    "# scrna_matrix_filepath = [Path(f\"data/results/{context}/scrna/scrna_{context}.h5ad\") for context in context_names]\n"
-   ]
+    "# Output Components\n"
+   ],
+   "outputs": [],
+   "execution_count": 11
   },
   {
    "cell_type": "markdown",
@@ -273,10 +281,13 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-16T20:07:08.331063Z",
+     "start_time": "2024-12-16T20:06:10.188429Z"
+    }
+   },
    "source": [
     "for context in context_names:\n",
     "    if context not in {*trna_matrix_filepath, *mrna_matrix_filepath}:\n",
@@ -287,14 +298,27 @@
     "        como_context_dir=como_context_dir[context],\n",
     "        input_matrix_filepath=None,\n",
     "        output_gene_info_filepath=gene_info_filepath[context],\n",
-    "        output_trna_config_filepath=trna_metadata_filepath,\n",
+    "        output_trna_metadata_filepath=trna_metadata_filepath,\n",
     "        output_trna_count_matrix_filepath=trna_matrix_filepath[context],\n",
-    "        output_mrna_config_filepath=mrna_metadata_filepath,\n",
+    "        output_mrna_metadata_filepath=mrna_metadata_filepath,\n",
     "        output_mrna_count_matrix_filepath=mrna_matrix_filepath[context],\n",
     "        cache=True,\n",
     "        log_level=\"INFO\",\n",
     "    )"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001B[32m2024-12-16 14:06:13\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m277\u001B[0m - \u001B[32m\u001B[1mWrote gene count matrix for 'mrna' RNA at '/Users/joshl/Projects/COMO/main/data/results/naiveB/polya-rna/polyarna_naiveB.csv'\u001B[0m\n",
+      "\u001B[32m2024-12-16 14:06:13\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m277\u001B[0m - \u001B[32m\u001B[1mWrote gene count matrix for 'total' RNA at '/Users/joshl/Projects/COMO/main/data/results/naiveB/total-rna/totalrna_naiveB.csv'\u001B[0m\n",
+      "\u001B[32m2024-12-16 14:06:13\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m468\u001B[0m - \u001B[1mFetching gene info (this may take 1-5 minutes depending on the number of genes and your internet connection)\u001B[0m\n",
+      "\u001B[32m2024-12-16 14:07:08\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m505\u001B[0m - \u001B[32m\u001B[1mGene Info file written at '/Users/joshl/Projects/COMO/main/data/results/naiveB/gene_info.csv'\u001B[0m\n"
+     ]
+    }
+   ],
+   "execution_count": 8
   },
   {
    "cell_type": "markdown",
@@ -367,10 +391,13 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-16T20:08:00.274368Z",
+     "start_time": "2024-12-16T20:07:48.637980Z"
+    }
+   },
    "source": [
     "from como.rnaseq_gen import FilteringTechnique, rnaseq_gen\n",
     "\n",
@@ -378,9 +405,10 @@
     "high_confidence_replicate_ratio = 1.0\n",
     "batch_ratio = 0.75\n",
     "high_confidence_batch_ratio = 1.0\n",
-    "technique = FilteringTechnique.zfpkm\n",
+    "technique = FilteringTechnique.ZFPKM\n",
     "cutoff = -3\n",
     "\n",
+    "\n",
     "for context in context_names:\n",
     "    if context not in trna_matrix_filepath:\n",
     "        continue\n",
@@ -388,9 +416,9 @@
     "        context_name=context,\n",
     "        input_rnaseq_filepath=trna_matrix_filepath[context],\n",
     "        input_gene_info_filepath=gene_info_filepath[context],\n",
-    "        output_rnaseq_filepath=trna_matrix_filepath[context],\n",
-    "        prep=RNAPrepMethod.TOTAL,\n",
-    "        taxon=taxon_id,\n",
+    "        output_boolean_activity_filepath=trna_matrix_filepath[context],\n",
+    "        prep=RNAType.TRNA,\n",
+    "        taxon_id=taxon_id,\n",
     "        input_metadata_filepath=trna_metadata_filepath,\n",
     "        replicate_ratio=replicate_ratio,\n",
     "        high_replicate_ratio=high_confidence_replicate_ratio,\n",
@@ -399,7 +427,20 @@
     "        technique=technique,\n",
     "        cutoff=cutoff\n",
     "    )"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/joshl/Projects/COMO/main/como/rnaseq_gen.py:430: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "  mega_df = pd.concat([mega_df, df], ignore_index=True)\n",
+      "\u001B[32m2024-12-16 14:08:00\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36mcomo.rnaseq_gen\u001B[0m:\u001B[36m691\u001B[0m - \u001B[1mnaiveB - Found 9492 expressed and 9492 confidently expressed genes\u001B[0m\n",
+      "\u001B[32m2024-12-16 14:08:00\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_gen\u001B[0m:\u001B[36m694\u001B[0m - \u001B[32m\u001B[1mWrote boolean matrix to data/results/naiveB/total-rna/totalrna_naiveB.csv\u001B[0m\n"
+     ]
+    }
+   ],
+   "execution_count": 9
   },
   {
    "cell_type": "markdown",
@@ -421,10 +462,13 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-16T20:15:16.139743Z",
+     "start_time": "2024-12-16T20:15:15.998158Z"
+    }
+   },
    "source": [
     "from como.rnaseq_gen import FilteringTechnique, rnaseq_gen\n",
     "\n",
@@ -432,7 +476,7 @@
     "high_confidence_replicate_ratio = 1.0\n",
     "batch_ratio = 0.75\n",
     "high_confidence_batch_ratio = 1.0\n",
-    "technique = FilteringTechnique.zfpkm\n",
+    "technique = FilteringTechnique.ZFPKM\n",
     "cutoff = -3\n",
     "\n",
     "for context in context_names:\n",
@@ -442,9 +486,9 @@
     "        context_name=context,\n",
     "        input_rnaseq_filepath=mrna_matrix_filepath[context],\n",
     "        input_gene_info_filepath=gene_info_filepath[context],\n",
-    "        output_rnaseq_filepath=mrna_matrix_filepath[context],\n",
-    "        prep=RNAPrepMethod.MRNA,\n",
-    "        taxon=taxon_id,\n",
+    "        output_boolean_activity_filepath=mrna_matrix_filepath[context],\n",
+    "        prep=RNAType.MRNA,\n",
+    "        taxon_id=taxon_id,\n",
     "        input_metadata_filepath=mrna_metadata_filepath,\n",
     "        replicate_ratio=replicate_ratio,\n",
     "        high_replicate_ratio=high_confidence_replicate_ratio,\n",
@@ -453,7 +497,34 @@
     "        technique=technique,\n",
     "        cutoff=cutoff\n",
     "    )"
-   ]
+   ],
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'ensembl_gene_id'",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[0;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
+      "File \u001B[0;32m~/Projects/COMO/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[0;34m(self, key)\u001B[0m\n\u001B[1;32m   3804\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m-> 3805\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_engine\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcasted_key\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   3806\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n",
+      "File \u001B[0;32mindex.pyx:167\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[0;34m()\u001B[0m\n",
+      "File \u001B[0;32mindex.pyx:196\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[0;34m()\u001B[0m\n",
+      "File \u001B[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[0;34m()\u001B[0m\n",
+      "File \u001B[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[0;34m()\u001B[0m\n",
+      "\u001B[0;31mKeyError\u001B[0m: 'ensembl_gene_id'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001B[0;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
+      "Cell \u001B[0;32mIn[15], line 13\u001B[0m\n\u001B[1;32m     11\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m context \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m mrna_matrix_filepath:\n\u001B[1;32m     12\u001B[0m     \u001B[38;5;28;01mcontinue\u001B[39;00m\n\u001B[0;32m---> 13\u001B[0m \u001B[38;5;28;01mawait\u001B[39;00m rnaseq_gen(\n\u001B[1;32m     14\u001B[0m     context_name\u001B[38;5;241m=\u001B[39mcontext,\n\u001B[1;32m     15\u001B[0m     input_rnaseq_filepath\u001B[38;5;241m=\u001B[39mmrna_matrix_filepath[context],\n\u001B[1;32m     16\u001B[0m     input_gene_info_filepath\u001B[38;5;241m=\u001B[39mgene_info_filepath[context],\n\u001B[1;32m     17\u001B[0m     output_rnaseq_filepath\u001B[38;5;241m=\u001B[39mmrna_matrix_filepath[context],\n\u001B[1;32m     18\u001B[0m     prep\u001B[38;5;241m=\u001B[39mRNAPrepMethod\u001B[38;5;241m.\u001B[39mMRNA,\n\u001B[1;32m     19\u001B[0m     taxon\u001B[38;5;241m=\u001B[39mtaxon_id,\n\u001B[1;32m     20\u001B[0m     input_metadata_filepath\u001B[38;5;241m=\u001B[39mmrna_metadata_filepath,\n\u001B[1;32m     21\u001B[0m     replicate_ratio\u001B[38;5;241m=\u001B[39mreplicate_ratio,\n\u001B[1;32m     22\u001B[0m     high_replicate_ratio\u001B[38;5;241m=\u001B[39mhigh_confidence_replicate_ratio,\n\u001B[1;32m     23\u001B[0m     batch_ratio\u001B[38;5;241m=\u001B[39mbatch_ratio,\n\u001B[1;32m     24\u001B[0m     high_batch_ratio\u001B[38;5;241m=\u001B[39mhigh_confidence_batch_ratio,\n\u001B[1;32m     25\u001B[0m     technique\u001B[38;5;241m=\u001B[39mtechnique,\n\u001B[1;32m     26\u001B[0m     cutoff\u001B[38;5;241m=\u001B[39mcutoff\n\u001B[1;32m     27\u001B[0m )\n",
+      "File \u001B[0;32m~/Projects/COMO/main/como/rnaseq_gen.py:787\u001B[0m, in \u001B[0;36mrnaseq_gen\u001B[0;34m(context_name, input_rnaseq_filepath, input_gene_info_filepath, output_rnaseq_filepath, prep, taxon, input_metadata_filepath, input_metadata_df, replicate_ratio, high_replicate_ratio, batch_ratio, high_batch_ratio, technique, cutoff)\u001B[0m\n\u001B[1;32m    784\u001B[0m logger\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mStarting \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext_name\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m    785\u001B[0m output_rnaseq_filepath\u001B[38;5;241m.\u001B[39mparent\u001B[38;5;241m.\u001B[39mmkdir(parents\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, exist_ok\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[0;32m--> 787\u001B[0m \u001B[38;5;28;01mawait\u001B[39;00m _save_rnaseq_tests(\n\u001B[1;32m    788\u001B[0m     context_name\u001B[38;5;241m=\u001B[39mcontext_name,\n\u001B[1;32m    789\u001B[0m     rnaseq_matrix\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mawait\u001B[39;00m _read_counts(input_rnaseq_filepath),\n\u001B[1;32m    790\u001B[0m     metadata_df\u001B[38;5;241m=\u001B[39minput_metadata_df \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28;01mawait\u001B[39;00m _create_metadata_df(input_metadata_filepath),\n\u001B[1;32m    791\u001B[0m     gene_info_df\u001B[38;5;241m=\u001B[39mpd\u001B[38;5;241m.\u001B[39mread_csv(input_gene_info_filepath),\n\u001B[1;32m    792\u001B[0m     output_filepath\u001B[38;5;241m=\u001B[39moutput_rnaseq_filepath,\n\u001B[1;32m    793\u001B[0m     prep\u001B[38;5;241m=\u001B[39mprep,\n\u001B[1;32m    794\u001B[0m     taxon\u001B[38;5;241m=\u001B[39mtaxon,\n\u001B[1;32m    795\u001B[0m     replicate_ratio\u001B[38;5;241m=\u001B[39mreplicate_ratio,\n\u001B[1;32m    796\u001B[0m     batch_ratio\u001B[38;5;241m=\u001B[39mbatch_ratio,\n\u001B[1;32m    797\u001B[0m     high_replicate_ratio\u001B[38;5;241m=\u001B[39mhigh_replicate_ratio,\n\u001B[1;32m    798\u001B[0m     high_batch_ratio\u001B[38;5;241m=\u001B[39mhigh_batch_ratio,\n\u001B[1;32m    799\u001B[0m     technique\u001B[38;5;241m=\u001B[39mtechnique,\n\u001B[1;32m    800\u001B[0m     cut_off\u001B[38;5;241m=\u001B[39mcutoff,\n\u001B[1;32m    801\u001B[0m )\n",
+      "File \u001B[0;32m~/Projects/COMO/main/como/rnaseq_gen.py:645\u001B[0m, in \u001B[0;36m_save_rnaseq_tests\u001B[0;34m(context_name, rnaseq_matrix, metadata_df, gene_info_df, output_filepath, prep, taxon, replicate_ratio, batch_ratio, high_replicate_ratio, high_batch_ratio, technique, cut_off)\u001B[0m\n\u001B[1;32m    636\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Save the results of the RNA-Seq tests to a CSV file.\"\"\"\u001B[39;00m\n\u001B[1;32m    637\u001B[0m filtering_options \u001B[38;5;241m=\u001B[39m _FilteringOptions(\n\u001B[1;32m    638\u001B[0m     replicate_ratio\u001B[38;5;241m=\u001B[39mreplicate_ratio,\n\u001B[1;32m    639\u001B[0m     batch_ratio\u001B[38;5;241m=\u001B[39mbatch_ratio,\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    642\u001B[0m     high_batch_ratio\u001B[38;5;241m=\u001B[39mhigh_batch_ratio,\n\u001B[1;32m    643\u001B[0m )\n\u001B[0;32m--> 645\u001B[0m read_counts_results: _ReadMatrixResults \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m _build_matrix_results(\n\u001B[1;32m    646\u001B[0m     matrix\u001B[38;5;241m=\u001B[39mrnaseq_matrix,\n\u001B[1;32m    647\u001B[0m     gene_info\u001B[38;5;241m=\u001B[39mgene_info_df,\n\u001B[1;32m    648\u001B[0m     metadata_df\u001B[38;5;241m=\u001B[39mmetadata_df,\n\u001B[1;32m    649\u001B[0m     taxon\u001B[38;5;241m=\u001B[39mtaxon,\n\u001B[1;32m    650\u001B[0m )\n\u001B[1;32m    651\u001B[0m metrics \u001B[38;5;241m=\u001B[39m read_counts_results\u001B[38;5;241m.\u001B[39mmetrics\n\u001B[1;32m    652\u001B[0m entrez_gene_ids \u001B[38;5;241m=\u001B[39m read_counts_results\u001B[38;5;241m.\u001B[39mentrez_gene_ids\n",
+      "File \u001B[0;32m~/Projects/COMO/main/como/rnaseq_gen.py:175\u001B[0m, in \u001B[0;36m_build_matrix_results\u001B[0;34m(matrix, gene_info, metadata_df, taxon)\u001B[0m\n\u001B[1;32m    167\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Read the counts matrix and returns the results.\u001B[39;00m\n\u001B[1;32m    168\u001B[0m \n\u001B[1;32m    169\u001B[0m \u001B[38;5;124;03m:param matrix: The gene counts matrix to process\u001B[39;00m\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    172\u001B[0m \u001B[38;5;124;03m:return: A dataclass `ReadMatrixResults`\u001B[39;00m\n\u001B[1;32m    173\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m    174\u001B[0m gene_info \u001B[38;5;241m=\u001B[39m gene_info_migrations(gene_info)\n\u001B[0;32m--> 175\u001B[0m conversion \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m ensembl_to_gene_id_and_symbol(ids\u001B[38;5;241m=\u001B[39m\u001B[43mmatrix\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mensembl_gene_id\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241m.\u001B[39mtolist(), taxon\u001B[38;5;241m=\u001B[39mtaxon)\n\u001B[1;32m    176\u001B[0m matrix \u001B[38;5;241m=\u001B[39m matrix\u001B[38;5;241m.\u001B[39mmerge(conversion, on\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mensembl_gene_id\u001B[39m\u001B[38;5;124m\"\u001B[39m, how\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mleft\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m    178\u001B[0m \u001B[38;5;66;03m# Only include Entrez and Ensembl Gene IDs that are present in `gene_info`\u001B[39;00m\n",
+      "File \u001B[0;32m~/Projects/COMO/.venv/lib/python3.10/site-packages/pandas/core/frame.py:4102\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[0;34m(self, key)\u001B[0m\n\u001B[1;32m   4100\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcolumns\u001B[38;5;241m.\u001B[39mnlevels \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[1;32m   4101\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_getitem_multilevel(key)\n\u001B[0;32m-> 4102\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolumns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   4103\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_integer(indexer):\n\u001B[1;32m   4104\u001B[0m     indexer \u001B[38;5;241m=\u001B[39m [indexer]\n",
+      "File \u001B[0;32m~/Projects/COMO/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[0;34m(self, key)\u001B[0m\n\u001B[1;32m   3807\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(casted_key, \u001B[38;5;28mslice\u001B[39m) \u001B[38;5;129;01mor\u001B[39;00m (\n\u001B[1;32m   3808\u001B[0m         \u001B[38;5;28misinstance\u001B[39m(casted_key, abc\u001B[38;5;241m.\u001B[39mIterable)\n\u001B[1;32m   3809\u001B[0m         \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28many\u001B[39m(\u001B[38;5;28misinstance\u001B[39m(x, \u001B[38;5;28mslice\u001B[39m) \u001B[38;5;28;01mfor\u001B[39;00m x \u001B[38;5;129;01min\u001B[39;00m casted_key)\n\u001B[1;32m   3810\u001B[0m     ):\n\u001B[1;32m   3811\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m InvalidIndexError(key)\n\u001B[0;32m-> 3812\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[1;32m   3813\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[1;32m   3814\u001B[0m     \u001B[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001B[39;00m\n\u001B[1;32m   3815\u001B[0m     \u001B[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001B[39;00m\n\u001B[1;32m   3816\u001B[0m     \u001B[38;5;66;03m#  the TypeError.\u001B[39;00m\n\u001B[1;32m   3817\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_indexing_error(key)\n",
+      "\u001B[0;31mKeyError\u001B[0m: 'ensembl_gene_id'"
+     ]
+    }
+   ],
+   "execution_count": 15
   },
   {
    "cell_type": "markdown",
@@ -475,10 +546,10 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from como.rnaseq_gen import FilteringTechnique, rnaseq_gen\n",
     "\n",
@@ -486,7 +557,7 @@
     "high_confidence_replicate_ratio = 1.0\n",
     "batch_ratio = 0.75\n",
     "high_confidence_batch_ratio = 1.0\n",
-    "technique = FilteringTechnique.umi\n",
+    "technique = FilteringTechnique.UMI\n",
     "cutoff = -3\n",
     "\n",
     "for context in context_names:\n",
@@ -496,9 +567,9 @@
     "        context_name=context,\n",
     "        input_rnaseq_filepath=scrna_matrix_filepath[context],\n",
     "        input_gene_info_filepath=gene_info_filepath[context],\n",
-    "        output_rnaseq_filepath=scrna_matrix_filepath[context],\n",
-    "        prep=RNAPrepMethod.SCRNA,\n",
-    "        taxon=taxon_id,\n",
+    "        output_boolean_activity_filepath=scrna_matrix_filepath[context],\n",
+    "        prep=RNAType.SCRNA,\n",
+    "        taxon_id=taxon_id,\n",
     "        input_metadata_filepath=Path(\"./data/config_sheets/scrna_config.xlsx\"),\n",
     "        replicate_ratio=replicate_ratio,\n",
     "        high_replicate_ratio=high_confidence_replicate_ratio,\n",
@@ -526,10 +597,10 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from como.proteomics_gen import proteomics_gen\n",
     "\n",
@@ -552,8 +623,8 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Cluster Sample Data (Optional)\n",
     "This step is used to cluster the samples based on their expression values. This can be used to determine which samples are more similar to each other. In a perfect world, one cluster would be created for each context type used. This is done using the `como/cluster_rnaseq.py` script.\n",
@@ -577,10 +648,10 @@
    ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "filt_technique = \"zfpkm\"\n",
     "cluster_algorithm = \"umap\"\n",
@@ -660,39 +731,90 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-16T19:54:26.540397Z",
+     "start_time": "2024-12-16T19:54:26.515554Z"
+    }
+   },
    "source": [
-    "expression_requirement = 3\n",
-    "requirement_adjust = \"regressive\"\n",
+    "from como.merge_xomics import merge_xomics, AdjustmentMethod\n",
+    "\n",
     "total_rna_weight = 6\n",
     "mrna_weight = 6\n",
     "single_cell_weight = 6\n",
     "proteomics_weight = 10\n",
+    "minimum_source_expression = 1\n",
+    "expression_requirement = 1\n",
+    "requirement_adjustment_method = AdjustmentMethod.REGRESSIVE\n",
+    "force_activate_high_confidence = False\n",
+    "adjust_for_na_sources = False\n",
+    "merge_zfpkm_distrubution = True\n",
+    "keep_transcriptomics_score = True\n",
     "\n",
-    "# fmt: off\n",
-    "cmd = \" \".join(\n",
-    "    [\n",
-    "        \"python3\", \"como/merge_xomics.py\",\n",
-    "        \"--merge-zfpkm-distribution\",\n",
-    "        \"--total-rnaseq-config-file\", trnaseq_config_file,  # noqa: F821\n",
-    "        \"--mrnaseq-config-file\", mrnaseq_config_file,  # noqa: F821\n",
-    "        # \"--scrnaseq-config-file\", scrnaseq_config_file,  # If using single-cell data, uncomment the start of this line\n",
-    "        # \"--proteomics-config-file\", proteomics_config_file,  # If using proteomics data, uncomment the start of this line\n",
-    "        \"--requirement-adjust\", requirement_adjust,\n",
-    "        \"--expression-requirement\", str(expression_requirement),\n",
-    "        \"--total-rnaseq-weight\", str(total_rna_weight),\n",
-    "        \"--mrnaseq-weight\", str(mrna_weight),\n",
-    "        \"--single-cell-rnaseq-weight\", str(single_cell_weight),\n",
-    "        \"--protein-weight\", str(proteomics_weight),\n",
-    "        \"--no-high-confidence\",\n",
-    "    ]\n",
-    ")\n",
-    "# fmt: on\n",
     "\n",
-    "!{cmd}"
-   ]
+    "mrna_batches = {\"naiveB\": [\n",
+    "    \"naiveB_S1R1\", \"naiveB_S1R2\", \"naiveB_S1R3\", \"naiveB_S1R4\",\n",
+    "    \"naiveB_S2R1\", \"naiveB_S2R2\", \"naiveB_S2R3\", \"naiveB_S2R4\",\n",
+    "]}\n",
+    "trna_batches = {\"naiveB\": [\"naiveB_S3R1\", \"naiveB_S3R2\", \"naiveB_S3R3\"]}\n",
+    "\n",
+    "\n",
+    "for context in context_names:\n",
+    "    await merge_xomics(\n",
+    "        context_name=context,\n",
+    "        trna_matrix_or_filepath=trna_matrix_filepath[context],\n",
+    "        mrna_matrix_or_filepath=mrna_matrix_filepath[context],\n",
+    "        scrna_matrix_or_filepath=None,  # scrna_matrix_filepath[context],\n",
+    "        proteomic_matrix_or_filepath=None,  # proteomics_matrix_filepath[context],\n",
+    "        trna_batches=trna_batches,\n",
+    "        mrna_batches=mrna_batches,\n",
+    "        scrna_batches=None,\n",
+    "        proteomic_batches=None,\n",
+    "        trna_weight=total_rna_weight,\n",
+    "        mrna_weight=mrna_weight,\n",
+    "        scrna_weight=single_cell_weight,\n",
+    "        proteomic_weight=proteomics_weight,\n",
+    "        minimum_source_expression=minimum_source_expression,\n",
+    "        expression_requirement=expression_requirement,\n",
+    "        adjust_method=requirement_adjustment_method,\n",
+    "        force_activate_high_confidence=force_activate_high_confidence,\n",
+    "        adjust_for_na=adjust_for_na_sources,\n",
+    "        merge_zfpkm_distribution=merge_zfpkm_distrubution,\n",
+    "        keep_transcriptomics_score=keep_transcriptomics_score,\n",
+    "        output_merge_activity_filepath=Path(f\"data/results/{context}/ActiveGenes_{context}_Merged.csv\"),\n",
+    "        output_transcriptomic_details_filepath=Path(f\"data/results/{context}/TranscriptomicDetails_{context}.csv\"),\n",
+    "        output_trna_activity_filepath=Path(f\"data/results/{context}/total-rna/trna_activity_{context}.csv\"),\n",
+    "        output_mrna_activity_filepath=Path(f\"data/results/{context}/mrna/mrna_activity_{context}.csv\"),\n",
+    "        output_scrna_activity_filepath=Path(f\"data/results/{context}/scrna/scrna_activity_{context}.csv\"),\n",
+    "        output_proteomic_activity_filepath=Path(f\"data/results/{context}/proteomics/proteomic_activity_{context}.csv\"),\n",
+    "        output_final_model_scores_filepath=Path(f\"data/results/{context}/model_scores_{context}.csv\"),\n",
+    "        output_figure_dirpath=Path(f\"data/results/{context}/figures\")\n",
+    "    )"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "data/results/naiveB/total-rna/trna_activity_naiveB.csv\n"
+     ]
+    },
+    {
+     "ename": "FileExistsError",
+     "evalue": "[Errno 17] File exists: 'data/results/naiveB/total-rna'",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[0;31mFileExistsError\u001B[0m                           Traceback (most recent call last)",
+      "Cell \u001B[0;32mIn[6], line 24\u001B[0m\n\u001B[1;32m     20\u001B[0m trna_batches \u001B[38;5;241m=\u001B[39m {\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnaiveB\u001B[39m\u001B[38;5;124m\"\u001B[39m: [\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnaiveB_S3R1\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnaiveB_S3R2\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnaiveB_S3R3\u001B[39m\u001B[38;5;124m\"\u001B[39m]}\n\u001B[1;32m     23\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m context \u001B[38;5;129;01min\u001B[39;00m context_names:\n\u001B[0;32m---> 24\u001B[0m     \u001B[38;5;28;01mawait\u001B[39;00m merge_xomics(\n\u001B[1;32m     25\u001B[0m         context_name\u001B[38;5;241m=\u001B[39mcontext_names,\n\u001B[1;32m     26\u001B[0m         trna_matrix_or_filepath\u001B[38;5;241m=\u001B[39mtrna_matrix_filepath[context],\n\u001B[1;32m     27\u001B[0m         mrna_matrix_or_filepath\u001B[38;5;241m=\u001B[39mmrna_matrix_filepath[context],\n\u001B[1;32m     28\u001B[0m         scrna_matrix_or_filepath\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,  \u001B[38;5;66;03m# scrna_matrix_filepath[context],\u001B[39;00m\n\u001B[1;32m     29\u001B[0m         proteomic_matrix_or_filepath\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,  \u001B[38;5;66;03m# proteomics_matrix_filepath[context],\u001B[39;00m\n\u001B[1;32m     30\u001B[0m         trna_batches\u001B[38;5;241m=\u001B[39mtrna_batches,\n\u001B[1;32m     31\u001B[0m         mrna_batches\u001B[38;5;241m=\u001B[39mmrna_batches,\n\u001B[1;32m     32\u001B[0m         scrna_batches\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[1;32m     33\u001B[0m         proteomic_batches\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[1;32m     34\u001B[0m         trna_weight\u001B[38;5;241m=\u001B[39mtotal_rna_weight,\n\u001B[1;32m     35\u001B[0m         mrna_weight\u001B[38;5;241m=\u001B[39mmrna_weight,\n\u001B[1;32m     36\u001B[0m         scrna_weight\u001B[38;5;241m=\u001B[39msingle_cell_weight,\n\u001B[1;32m     37\u001B[0m         proteomic_weight\u001B[38;5;241m=\u001B[39mproteomics_weight,\n\u001B[1;32m     38\u001B[0m         minimum_source_expression\u001B[38;5;241m=\u001B[39mminimum_source_expression,\n\u001B[1;32m     39\u001B[0m         expression_requirement\u001B[38;5;241m=\u001B[39mexpression_requirement,\n\u001B[1;32m     40\u001B[0m         adjust_method\u001B[38;5;241m=\u001B[39mrequirement_adjustment_method,\n\u001B[1;32m     41\u001B[0m         force_activate_high_confidence\u001B[38;5;241m=\u001B[39mforce_activate_high_confidence,\n\u001B[1;32m     42\u001B[0m         adjust_for_na\u001B[38;5;241m=\u001B[39madjust_for_na_sources,\n\u001B[1;32m     43\u001B[0m         merge_zfpkm_distribution\u001B[38;5;241m=\u001B[39mmerge_zfpkm_distrubution,\n\u001B[1;32m     44\u001B[0m         keep_transcriptomics_score\u001B[38;5;241m=\u001B[39mkeep_transcriptomics_score,\n\u001B[1;32m     45\u001B[0m         output_merge_activity_filepath\u001B[38;5;241m=\u001B[39mPath(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdata/results/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/ActiveGenes_\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m_Merged.csv\u001B[39m\u001B[38;5;124m\"\u001B[39m),\n\u001B[1;32m     46\u001B[0m         output_transcriptomic_details_filepath\u001B[38;5;241m=\u001B[39mPath(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdata/results/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/TranscriptomicDetails_\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.csv\u001B[39m\u001B[38;5;124m\"\u001B[39m),\n\u001B[1;32m     47\u001B[0m         output_trna_activity_filepath\u001B[38;5;241m=\u001B[39mPath(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdata/results/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/total-rna/trna_activity_\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.csv\u001B[39m\u001B[38;5;124m\"\u001B[39m),\n\u001B[1;32m     48\u001B[0m         output_mrna_activity_filepath\u001B[38;5;241m=\u001B[39mPath(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdata/results/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/mrna/mrna_activity_\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.csv\u001B[39m\u001B[38;5;124m\"\u001B[39m),\n\u001B[1;32m     49\u001B[0m         output_scrna_activity_filepath\u001B[38;5;241m=\u001B[39mPath(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdata/results/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/scrna/scrna_activity_\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.csv\u001B[39m\u001B[38;5;124m\"\u001B[39m),\n\u001B[1;32m     50\u001B[0m         output_proteomic_activity_filepath\u001B[38;5;241m=\u001B[39mPath(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdata/results/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/proteomics/proteomic_activity_\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.csv\u001B[39m\u001B[38;5;124m\"\u001B[39m),\n\u001B[1;32m     51\u001B[0m         output_final_model_scores_filepath\u001B[38;5;241m=\u001B[39mPath(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdata/results/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/model_scores_\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.csv\u001B[39m\u001B[38;5;124m\"\u001B[39m),\n\u001B[1;32m     52\u001B[0m         output_figure_dirpath\u001B[38;5;241m=\u001B[39mPath(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdata/results/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/figures\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m     53\u001B[0m     )\n",
+      "File \u001B[0;32m~/Projects/COMO/main/como/merge_xomics.py:678\u001B[0m, in \u001B[0;36mmerge_xomics\u001B[0;34m(context_name, trna_matrix_or_filepath, mrna_matrix_or_filepath, scrna_matrix_or_filepath, proteomic_matrix_or_filepath, trna_batches, mrna_batches, scrna_batches, proteomic_batches, output_merge_activity_filepath, output_transcriptomic_details_filepath, output_trna_activity_filepath, output_mrna_activity_filepath, output_scrna_activity_filepath, output_proteomic_activity_filepath, output_final_model_scores_filepath, output_figure_dirpath, trna_weight, mrna_weight, scrna_weight, proteomic_weight, minimum_source_expression, expression_requirement, adjust_method, force_activate_high_confidence, adjust_for_na, merge_zfpkm_distribution, keep_transcriptomics_score, weighted_z_floor, weighted_z_ceiling)\u001B[0m\n\u001B[1;32m    676\u001B[0m     output_transcriptomic_details_filepath\u001B[38;5;241m.\u001B[39mparent\u001B[38;5;241m.\u001B[39mmkdir(parents\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[1;32m    677\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m output_trna_activity_filepath \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m output_trna_activity_filepath\u001B[38;5;241m.\u001B[39mexists():\n\u001B[0;32m--> 678\u001B[0m     \u001B[43moutput_trna_activity_filepath\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mparent\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmkdir\u001B[49m\u001B[43m(\u001B[49m\u001B[43mparents\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[1;32m    679\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m output_mrna_activity_filepath \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m output_mrna_activity_filepath\u001B[38;5;241m.\u001B[39mexists():\n\u001B[1;32m    680\u001B[0m     output_mrna_activity_filepath\u001B[38;5;241m.\u001B[39mparent\u001B[38;5;241m.\u001B[39mmkdir(parents\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n",
+      "File \u001B[0;32m~/.local/share/uv/python/cpython-3.10.15-macos-aarch64-none/lib/python3.10/pathlib.py:1175\u001B[0m, in \u001B[0;36mPath.mkdir\u001B[0;34m(self, mode, parents, exist_ok)\u001B[0m\n\u001B[1;32m   1171\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m   1172\u001B[0m \u001B[38;5;124;03mCreate a new directory at this given path.\u001B[39;00m\n\u001B[1;32m   1173\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m   1174\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m-> 1175\u001B[0m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_accessor\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmkdir\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmode\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   1176\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mFileNotFoundError\u001B[39;00m:\n\u001B[1;32m   1177\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m parents \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mparent \u001B[38;5;241m==\u001B[39m \u001B[38;5;28mself\u001B[39m:\n",
+      "\u001B[0;31mFileExistsError\u001B[0m: [Errno 17] File exists: 'data/results/naiveB/total-rna'"
+     ]
+    }
+   ],
+   "execution_count": 6
   },
   {
    "cell_type": "markdown",
diff --git a/main/como/__init__.py b/main/como/__init__.py
index c6ee72e2..c0a9a400 100644
--- a/main/como/__init__.py
+++ b/main/como/__init__.py
@@ -1,11 +1,6 @@
-import pandas as pd
-
-from como.project import Config
 from como.utils import stringlist_to_list
-
-__all__ = ["stringlist_to_list", "Config"]
-__version__ = "1.11.1"
+from como.data_types import LogLevel, FilteringTechnique, AdjustmentMethod, Solver
 
 
-def return_placeholder_data() -> pd.DataFrame:
-    return pd.DataFrame(data=0, index=pd.Index(data=[0], name="entrez_gene_id"), columns=["expressed", "top"])
+__all__ = ["stringlist_to_list", "LogLevel", "FilteringTechnique", "AdjustmentMethod", "Solver"]
+__version__ = "1.11.1"
diff --git a/main/como/utils.py b/main/como/utils.py
index 74d924cf..344ef03f 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -277,3 +277,6 @@ def _num_rows(item: pd.DataFrame | npt.NDArray) -> int:
 
 def _num_columns(item: pd.DataFrame | npt.NDArray) -> int:
     return item.shape[0]
+def return_placeholder_data() -> pd.DataFrame:
+    return pd.DataFrame(data=0, index=pd.Index(data=[0], name="entrez_gene_id"), columns=["expressed", "top"])
+

From b5d0a9145a34760b53eca4f60fd320d81cb816fa Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 13:53:07 -0600
Subject: [PATCH 126/345] fix: properly return row and column values

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/utils.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/main/como/utils.py b/main/como/utils.py
index 344ef03f..7bd125dd 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -272,11 +272,13 @@ def _listify(value):
 
 
 def _num_rows(item: pd.DataFrame | npt.NDArray) -> int:
-    return item.shape[1]
+    return item.shape[0]
 
 
 def _num_columns(item: pd.DataFrame | npt.NDArray) -> int:
-    return item.shape[0]
+    return item.shape[1]
+
+
 def return_placeholder_data() -> pd.DataFrame:
     return pd.DataFrame(data=0, index=pd.Index(data=[0], name="entrez_gene_id"), columns=["expressed", "top"])
 

From 4079deb64bc95d55e8c20f64cb57649e7ece70e9 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 13:53:53 -0600
Subject: [PATCH 127/345] feat: added function to set up logging throughout
 COMO

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/utils.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/main/como/utils.py b/main/como/utils.py
index 7bd125dd..18826666 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -282,3 +282,12 @@ def _num_columns(item: pd.DataFrame | npt.NDArray) -> int:
 def return_placeholder_data() -> pd.DataFrame:
     return pd.DataFrame(data=0, index=pd.Index(data=[0], name="entrez_gene_id"), columns=["expressed", "top"])
 
+
+def _set_up_logging(
+    level: LogLevel,
+    location: str | TextIOWrapper,
+    formatting: str = LOG_FORMAT,
+):
+    with contextlib.suppress(ValueError):
+        logger.remove(0)
+        logger.add(sink=location, level=level.value, format=formatting)

From bf7f139677207ed51675e69abf2115fd2ad21622 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 13:54:38 -0600
Subject: [PATCH 128/345] feat: added function to log and raise an error

---
 main/como/utils.py | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/main/como/utils.py b/main/como/utils.py
index 18826666..e3788878 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -291,3 +291,21 @@ def _set_up_logging(
     with contextlib.suppress(ValueError):
         logger.remove(0)
         logger.add(sink=location, level=level.value, format=formatting)
+
+
+def _log_and_raise_error(
+    message: str,
+    *,
+    error: type[BaseException],
+    level: LogLevel,
+) -> None:
+    caller = logger.opt(depth=1)
+    match level:
+        case LogLevel.ERROR:
+            caller.error(message)
+        case LogLevel.CRITICAL:
+            caller.critical(message)
+        case _:
+            raise ValueError(f"When raising an error, LogLevel.ERROR or LogLevel.CRITICAL must be used. Got: {level}")
+
+    raise error(message)

From 237d1fc5ffb87000a6df15d7767a548e7dfad0c9 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 13:55:25 -0600
Subject: [PATCH 129/345] refactor!: moved Algorithms to data_types

The Algorithms class fits better as a data type
---
 main/como/data_types.py |  5 +++++
 main/como/utils.py      | 21 ---------------------
 2 files changed, 5 insertions(+), 21 deletions(-)

diff --git a/main/como/data_types.py b/main/como/data_types.py
index a544aa8d..a9da5d56 100644
--- a/main/como/data_types.py
+++ b/main/como/data_types.py
@@ -25,6 +25,11 @@ def from_string(value: str) -> RNAPrepMethod:
             case _:
                 possible_values = [t.value for t in RNAPrepMethod]
                 raise ValueError(f"Filtering technique must be one of {possible_values}; got: {value}")
+class Algorithm(Enum):
+    GIMME = "GIMME"
+    FASTCORE = "FASTCORE"
+    IMAT = "IMAT"
+    TINIT = "TINIT"
 
 
 class FilteringTechnique(Enum):
diff --git a/main/como/utils.py b/main/como/utils.py
index e3788878..476d48cd 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -23,27 +23,6 @@
 __all__ = ["Compartments", "split_gene_expression_data", "stringlist_to_list", "suppress_stdout"]
 
 
-class Algorithm(Enum):
-    GIMME = "GIMME"
-    FASTCORE = "FASTCORE"
-    IMAT = "IMAT"
-    TINIT = "TINIT"
-
-    @staticmethod
-    def from_string(value: str) -> Algorithm:
-        match value.lower():
-            case "gimme":
-                return Algorithm.GIMME
-            case "fastcore":
-                return Algorithm.FASTCORE
-            case "imat":
-                return Algorithm.IMAT
-            case "tinit":
-                return Algorithm.TINIT
-            case _:
-                raise ValueError(f"Unknown solver: {value}")
-
-
 class Compartments:
     """Convert from compartment "long-hand" to "short-hand".
 

From 34ccd44ab0eaf7090dbd71946d60886b0188d74f Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 13:56:23 -0600
Subject: [PATCH 130/345] refactor!: moved Compartments to data_types

Renamed to CobraCompartments
---
 main/como/data_types.py | 68 +++++++++++++++++++++++++++++++++++++++++
 main/como/utils.py      | 61 ------------------------------------
 2 files changed, 68 insertions(+), 61 deletions(-)

diff --git a/main/como/data_types.py b/main/como/data_types.py
index a9da5d56..95f99f7a 100644
--- a/main/como/data_types.py
+++ b/main/como/data_types.py
@@ -60,3 +60,71 @@ def from_string(value: str) -> FilteringTechnique:
 PATH_TYPE = str | Path
 RNA_TYPE = Literal["total", "mrna"]
 LOG_LEVEL = Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]
+
+class CobraCompartments:
+    """Convert from compartment "long-hand" to "short-hand".
+
+    Shorthand from: https://cobrapy.readthedocs.io/en/latest/_modules/cobra/medium/annotations.html
+
+    "Extracellular" -> "e"
+    "golgi" -> "g"
+    """
+
+    SHORTHAND: ClassVar[dict[str, list[str]]] = {
+        "ce": ["cell envelope"],
+        "c": [
+            "cytoplasm",
+            "cytosol",
+            "default",
+            "in",
+            "intra cellular",
+            "intracellular",
+            "intracellular region",
+            "intracellular space",
+        ],
+        "er": ["endoplasmic reticulum"],
+        "erm": ["endoplasmic reticulum membrane"],
+        "e": [
+            "extracellular",
+            "extraorganism",
+            "out",
+            "extracellular space",
+            "extra organism",
+            "extra cellular",
+            "extra-organism",
+            "external",
+            "external medium",
+        ],
+        "f": ["flagellum", "bacterial-type flagellum"],
+        "g": ["golgi", "golgi apparatus"],
+        "gm": ["golgi membrane"],
+        "h": ["chloroplast"],
+        "l": ["lysosome"],
+        "im": ["mitochondrial intermembrane space"],
+        "mm": ["mitochondrial membrane"],
+        "m": ["mitochondrion", "mitochondria"],
+        "n": ["nucleus"],
+        "p": ["periplasm", "periplasmic space"],
+        "x": ["peroxisome", "glyoxysome"],
+        "u": ["thylakoid"],
+        "vm": ["vacuolar membrane"],
+        "v": ["vacuole"],
+        "w": ["cell wall"],
+        "s": ["eyespot", "eyespot apparatus", "stigma"],
+    }
+
+    _REVERSE_LOOKUP: ClassVar[dict[str, list[str]]] = {
+        value.lower(): key for key, values in SHORTHAND.items() for value in values
+    }
+
+    @classmethod
+    def get_shorthand(cls, longhand: str) -> str | None:
+        """Get the short-hand compartment name from the long-hand name."""
+        return cls._REVERSE_LOOKUP.get(longhand.lower(), None)
+
+    @classmethod
+    def get_longhand(cls, shorthand: str) -> str | None:
+        """Get the long-hand compartment name from the short-hand name."""
+        longhand = cls.SHORTHAND.get(shorthand.lower(), None)
+        return longhand[0] if longhand else None
+
diff --git a/main/como/utils.py b/main/como/utils.py
index 476d48cd..c87eb8d7 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -23,67 +23,6 @@
 __all__ = ["Compartments", "split_gene_expression_data", "stringlist_to_list", "suppress_stdout"]
 
 
-class Compartments:
-    """Convert from compartment "long-hand" to "short-hand".
-
-    Shorthand from: https://cobrapy.readthedocs.io/en/latest/_modules/cobra/medium/annotations.html
-
-    "Extracellular" -> "e"
-    "golgi" -> "g"
-    """
-
-    SHORTHAND: ClassVar[dict[str, list[str]]] = {
-        "ce": ["cell envelope"],
-        "c": [
-            "cytoplasm",
-            "cytosol",
-            "default",
-            "in",
-            "intra cellular",
-            "intracellular",
-            "intracellular region",
-            "intracellular space",
-        ],
-        "er": ["endoplasmic reticulum"],
-        "erm": ["endoplasmic reticulum membrane"],
-        "e": [
-            "extracellular",
-            "extraorganism",
-            "out",
-            "extracellular space",
-            "extra organism",
-            "extra cellular",
-            "extra-organism",
-            "external",
-            "external medium",
-        ],
-        "f": ["flagellum", "bacterial-type flagellum"],
-        "g": ["golgi", "golgi apparatus"],
-        "gm": ["golgi membrane"],
-        "h": ["chloroplast"],
-        "l": ["lysosome"],
-        "im": ["mitochondrial intermembrane space"],
-        "mm": ["mitochondrial membrane"],
-        "m": ["mitochondrion", "mitochondria"],
-        "n": ["nucleus"],
-        "p": ["periplasm", "periplasmic space"],
-        "x": ["peroxisome", "glyoxysome"],
-        "u": ["thylakoid"],
-        "vm": ["vacuolar membrane"],
-        "v": ["vacuole"],
-        "w": ["cell wall"],
-        "s": ["eyespot", "eyespot apparatus", "stigma"],
-    }
-
-    _REVERSE_LOOKUP: ClassVar[dict[str, list[str]]] = {
-        value.lower(): key for key, values in SHORTHAND.items() for value in values
-    }
-
-    @classmethod
-    def get(cls, longhand: str) -> str | None:
-        """Get the short-hand compartment name from the long-hand name."""
-        return cls._REVERSE_LOOKUP.get(longhand.lower(), None)
-
 
 def stringlist_to_list(stringlist: str | list[str]) -> list[str]:
     """Convert a string from the command line into a Python list.

From 038ab174fdcf9e007a6eaea4583d2a633440095b Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 13:56:58 -0600
Subject: [PATCH 131/345] refactor: re-organized constants

---
 main/como/data_types.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/main/como/data_types.py b/main/como/data_types.py
index 95f99f7a..a1cbc5b6 100644
--- a/main/como/data_types.py
+++ b/main/como/data_types.py
@@ -4,6 +4,13 @@
 from pathlib import Path
 from typing import Literal
 
+PATH_TYPE = str | Path
+LOG_FORMAT = (
+    "<green>{time:YYYY-MM-DD HH:mm:ss}</> | "
+    "<level>{level:<8}</> | "
+    "<cyan>{name}</>:<cyan>{line}</> - <level>{message}</>"
+)
+
 
 class RNAPrepMethod(Enum):
     TOTAL = "total"

From c337e4da747a8775f89875592a998254df27ef23 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 13:58:05 -0600
Subject: [PATCH 132/345] fix: removed items from __all__ that were moved

---
 main/como/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/utils.py b/main/como/utils.py
index c87eb8d7..05e8b5e3 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -20,8 +20,8 @@
 )
 from loguru import logger
 
-__all__ = ["Compartments", "split_gene_expression_data", "stringlist_to_list", "suppress_stdout"]
 
+__all__ = ["split_gene_expression_data", "stringlist_to_list", "suppress_stdout"]
 
 
 def stringlist_to_list(stringlist: str | list[str]) -> list[str]:

From 99676edef6ce1722b284d3191ec378162a658321 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 13:58:53 -0600
Subject: [PATCH 133/345] refactor: imported required packages and types

---
 main/como/utils.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/main/como/utils.py b/main/como/utils.py
index 05e8b5e3..5065c786 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -1,16 +1,21 @@
 from __future__ import annotations
 
+import asyncio
 import contextlib
+import functools
+import inspect
 import io
 import sys
 from collections.abc import Iterator
-from enum import Enum
+from concurrent.futures import ThreadPoolExecutor
+from io import TextIOWrapper
 from pathlib import Path
-from typing import ClassVar
+from typing import Union
 
 import aiofiles
 import numpy.typing as npt
 import pandas as pd
+import scanpy as sc
 from fast_bioservices import BioDBNet, Output, Taxon
 from fast_bioservices.pipeline import (
     determine_gene_type,
@@ -20,6 +25,7 @@
 )
 from loguru import logger
 
+from como.data_types import LOG_FORMAT, Algorithm, LogLevel
 
 __all__ = ["split_gene_expression_data", "stringlist_to_list", "suppress_stdout"]
 

From 8662f7c609b2269907ce4bee19933afc9839564e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:00:20 -0600
Subject: [PATCH 134/345] fix: added Algorithm as top level import

---
 main/como/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/__init__.py b/main/como/__init__.py
index c0a9a400..480993c9 100644
--- a/main/como/__init__.py
+++ b/main/como/__init__.py
@@ -1,5 +1,5 @@
 from como.utils import stringlist_to_list
-from como.data_types import LogLevel, FilteringTechnique, AdjustmentMethod, Solver
+from como.data_types import LogLevel, FilteringTechnique, AdjustmentMethod, Solver, Algorithm
 
 
 __all__ = ["stringlist_to_list", "LogLevel", "FilteringTechnique", "AdjustmentMethod", "Solver"]

From c454e8df97dd93a6f1dbf6b904de516925ab73e1 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:01:32 -0600
Subject: [PATCH 135/345] fix: added CobraCompartments as top-level import

---
 main/como/__init__.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/main/como/__init__.py b/main/como/__init__.py
index 480993c9..660fc21f 100644
--- a/main/como/__init__.py
+++ b/main/como/__init__.py
@@ -1,6 +1,14 @@
 from como.utils import stringlist_to_list
-from como.data_types import LogLevel, FilteringTechnique, AdjustmentMethod, Solver, Algorithm
+from como.data_types import LogLevel, FilteringTechnique, AdjustmentMethod, Solver, Algorithm, CobraCompartments
 
 
-__all__ = ["stringlist_to_list", "LogLevel", "FilteringTechnique", "AdjustmentMethod", "Solver"]
+__all__ = [
+    "AdjustmentMethod",
+    "Algorithm",
+    "CobraCompartments",
+    "FilteringTechnique",
+    "LogLevel",
+    "Solver",
+    "stringlist_to_list",
+]
 __version__ = "1.11.1"

From 510c77c751176b9da96b92a7478b90a66374b343 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:02:15 -0600
Subject: [PATCH 136/345] fix: process ids as string type

---
 main/como/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/utils.py b/main/como/utils.py
index 5065c786..164012d4 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -88,7 +88,7 @@ def split_gene_expression_data(
     single_gene_names = expression_data[~expression_data["entrez_gene_id"].astype(str).str.contains("//")]
     multiple_gene_names = expression_data[expression_data["entrez_gene_id"].astype(str).str.contains("//")]
     split_gene_names = multiple_gene_names.assign(
-        entrez_gene_id=multiple_gene_names["entrez_gene_id"].str.split("///")
+        entrez_gene_id=multiple_gene_names["entrez_gene_id"].astype(str).str.split("///")
     ).explode("entrez_gene_id")
 
     gene_expressions = pd.concat([single_gene_names, split_gene_names], axis=0, ignore_index=True)

From 3e934884fb826130e89f2ea6e76bff7b1bbd5c82 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:04:00 -0600
Subject: [PATCH 137/345] refactor: combine async file reading

Combine asynchronous file reading (csv, excel, etc.) into a single function
---
 main/como/utils.py | 44 ++++++++++++++++----------------------------
 1 file changed, 16 insertions(+), 28 deletions(-)

diff --git a/main/como/utils.py b/main/como/utils.py
index 164012d4..e14eedc3 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -130,40 +130,28 @@ async def _format_determination(
     return cohersion
 
 
-async def _async_read_csv(path: Path, **kwargs) -> pd.DataFrame:
-    """Asynchronously reads a CSV file and returns a pandas DataFrame.
+async def _read_file(
+    path: Path | io.StringIO | None,
+    h5ad_as_df: bool = True,
+    **kwargs,
+) -> pd.DataFrame | sc.AnnData | None:
+    """Asynchronously read a filepath and return a pandas DataFrame.
 
-    :param path: The path to read from
-    :param kwargs: Additional arguments to pass to pandas.read_csv
-    :return: A pandas DataFrame
-    """
-    if not path.exists():
-        raise FileNotFoundError(f"File {path} does not exist")
-    if path.suffix not in {".csv", ".tsv"}:
-        raise ValueError(f"File {path} is not a CSV file")
-
-    kwargs.setdefault("sep", "," if path.suffix == ".csv" else "\t")
-    async with aiofiles.open(path) as f:
-        content = await f.read()
-        return pd.read_csv(io.StringIO(content), **kwargs)
-
-
-async def _async_read_excel(path: Path, **kwargs) -> pd.DataFrame:
-    """Asynchronously reads an Excel file and returns a pandas DataFrame.
+    If the provided path is None, None will also be returned.
+    None may be provided to this function so that `asyncio.gather` can safely be used on all sources
+        (trna, mrna, scrna, proteomics) without needing to check if the user has provided those sources
 
     :param path: The path to read from
-    :param kwargs: Additional arguments to pass to pandas.read_excel
-    :return: A pandas DataFrame
+    :param kwargs: Additional arguments to pass to pandas.read_csv, pandas.read_excel,
+        or scanpy.read_h5ad, depending on the filepath provided
+    :return: None, or a pandas DataFrame or AnnData
     """
+    if not path:
+        return None
+
     if not path.exists():
+        logger.critical(f"File {path} does not exist")
         raise FileNotFoundError(f"File {path} does not exist")
-    if path.suffix not in {".xls", ".xlsx"}:
-        raise ValueError(f"File {path} is not an Excel file")
-
-    async with aiofiles.open(path, "rb") as f:
-        content = await f.read()
-        return pd.read_excel(io.StringIO(content.decode()), **kwargs)
-
 
 def is_notebook() -> bool:
     """Check if the current environment is a Jupyter Notebook.

From e99d3bd8313cd32013adc3465ae19e0efbb671a5 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:04:17 -0600
Subject: [PATCH 138/345] refactor: remove un-used function

---
 main/como/utils.py | 12 ------------
 1 file changed, 12 deletions(-)

diff --git a/main/como/utils.py b/main/como/utils.py
index e14eedc3..ac16f13f 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -153,18 +153,6 @@ async def _read_file(
         logger.critical(f"File {path} does not exist")
         raise FileNotFoundError(f"File {path} does not exist")
 
-def is_notebook() -> bool:
-    """Check if the current environment is a Jupyter Notebook.
-
-    :returns: True if the current environment is a Jupyter Notebook, False otherwise.
-    """
-    try:
-        from IPython import get_ipython
-
-        return get_ipython() is not None
-    except ModuleNotFoundError:
-        return False
-
 
 async def convert_gene_data(values: list[str], taxon_id: int | str | Taxon) -> pd.DataFrame:
     gene_type = await determine_gene_type(values)

From c1c6d00a1d6b66fe809fda698850057513c42986 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:08:14 -0600
Subject: [PATCH 139/345] refactor: fix async reading of data

---
 main/como/utils.py | 45 +++++++++++++++++++++++++++++++++++++--------
 1 file changed, 37 insertions(+), 8 deletions(-)

diff --git a/main/como/utils.py b/main/como/utils.py
index ac16f13f..8148f755 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -153,15 +153,44 @@ async def _read_file(
         logger.critical(f"File {path} does not exist")
         raise FileNotFoundError(f"File {path} does not exist")
 
+    # StringIO is used if a CSV file is read using open() directly
+    if isinstance(path, io.StringIO):
+        return pd.DataFrame(path, **kwargs)
+
+    match path.suffix:
+        case ".csv" | ".tsv":
+            kwargs.setdefault("sep", "," if path.suffix == ".csv" else "\t")
+            async with aiofiles.open(path) as f:
+                content = await f.read()
+                return pd.read_csv(io.StringIO(content), **kwargs)
+        case ".xlsx" | ".xls":
+            loop = asyncio.get_running_loop()
+            with ThreadPoolExecutor(max_workers=1) as pool:
+                # pass kwargs as args
+                func = functools.partial(pd.read_excel, **kwargs)
+                return await loop.run_in_executor(pool, func, path)
+        case ".h5ad":
+            loop = asyncio.get_running_loop()
+            with ThreadPoolExecutor(max_workers=1) as pool:
+                func = functools.partial(sc.read_h5ad, **kwargs)
+                adata: sc.AnnData = await loop.run_in_executor(pool, func, path)
+
+            if h5ad_as_df:
+                df = adata.to_df().T
+                df.index.name = "gene_symbol"
+                df.reset_index(inplace=True)
+                return df
+            return adata
+        case _:
+            logger.critical(
+                f"Unknown file extension '{path.suffix}'. Valid options are '.tsv', '.csv', '.xlsx', '.xls', or '.h5ad'"
+            )
+            raise ValueError(
+                f"Unknown file extension '{path.suffix}'. "
+                f"Valid options are '.tsv', '.csv', '.xlsx', '.xls', or '.h5ad'."
+            )
+
 
-async def convert_gene_data(values: list[str], taxon_id: int | str | Taxon) -> pd.DataFrame:
-    gene_type = await determine_gene_type(values)
-    if all(v == "gene_symbol" for v in gene_type.values()):
-        return await gene_symbol_to_ensembl_and_gene_id(values, taxon=taxon_id)
-    elif all(v == "ensembl_gene_id" for v in gene_type.values()):
-        return await ensembl_to_gene_id_and_symbol(ids=values, taxon=taxon_id)
-    elif all(v == "entrez_gene_id" for v in gene_type.values()):
-        return await gene_id_to_ensembl_and_gene_symbol(ids=values, taxon=taxon_id)
     else:
         raise ValueError("Gene data must be of the same type (i.e., all Ensembl, Entrez, or Gene Symbols)")
 

From 5c912b32ace05e5a483758c5ce33f59d57f1562f Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:08:30 -0600
Subject: [PATCH 140/345] feat: allow passing strings for dataframes

---
 main/como/utils.py | 25 ++++++++++++++++++++++++-
 1 file changed, 24 insertions(+), 1 deletion(-)

diff --git a/main/como/utils.py b/main/como/utils.py
index 8148f755..a25c8b3e 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -191,8 +191,31 @@ async def _read_file(
             )
 
 
+async def get_missing_gene_data(values: list[str] | pd.DataFrame, taxon_id: int | str | Taxon) -> pd.DataFrame:
+    if isinstance(values, list):
+        gene_type = await determine_gene_type(values)
+        if all(v == "gene_symbol" for v in gene_type.values()):
+            return await gene_symbol_to_ensembl_and_gene_id(values, taxon=taxon_id)
+        elif all(v == "ensembl_gene_id" for v in gene_type.values()):
+            return await ensembl_to_gene_id_and_symbol(ids=values, taxon=taxon_id)
+        elif all(v == "entrez_gene_id" for v in gene_type.values()):
+            return await gene_id_to_ensembl_and_gene_symbol(ids=values, taxon=taxon_id)
+        else:
+            logger.critical("Gene data must be of the same type (i.e., all Ensembl, Entrez, or Gene Symbols)")
+            raise ValueError("Gene data must be of the same type (i.e., all Ensembl, Entrez, or Gene Symbols)")
     else:
-        raise ValueError("Gene data must be of the same type (i.e., all Ensembl, Entrez, or Gene Symbols)")
+        values: pd.DataFrame  # Re-define type to assist in type hinting
+        if "gene_symbol" in values:
+            return await get_missing_gene_data(values["gene_symbol"].tolist(), taxon_id=taxon_id)
+        elif "entrez_gene_id" in values:
+            return await get_missing_gene_data(values["entrez_gene_id"].tolist(), taxon_id=taxon_id)
+        elif "ensembl_gene_id" in values:
+            return await get_missing_gene_data(values["ensembl_gene_id"].tolist(), taxon_id=taxon_id)
+        else:
+            logger.critical("Unable to find 'gene_symbol', 'entrez_gene_id', or 'ensembl_gene_id' in the input matrix.")
+            raise ValueError(
+                "Unable to find 'gene_symbol', 'entrez_gene_id', or 'ensembl_gene_id' in the input matrix."
+            )
 
 
 def _listify(value):

From e942b856d220c84e51d3192c7d3706c0869de30c Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:08:54 -0600
Subject: [PATCH 141/345] fix: importing of required and unused modules

---
 main/como/rnaseq_preprocess.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index b869a97e..ed598adc 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 import asyncio
-import contextlib
 import re
 import sys
 from dataclasses import dataclass, field
@@ -13,13 +12,12 @@
 import aiofiles
 import numpy as np
 import pandas as pd
-import scanpy as sc
 from fast_bioservices.biothings.mygene import MyGene
 from fast_bioservices.pipeline import ensembl_to_gene_id_and_symbol, gene_symbol_to_ensembl_and_gene_id
 from loguru import logger
 
-from como.data_types import LOG_LEVEL, PATH_TYPE, RNA_TYPE, RNAPrepMethod
-from como.utils import _listify
+from como.data_types import PATH_TYPE, LogLevel, RNAType
+from como.utils import _listify, _read_file, _set_up_logging
 
 
 @dataclass

From 0345a148c2645a93533c21bf393e55b25f80acd8 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:18:57 -0600
Subject: [PATCH 142/345] feat: use new log_and_raise_error

---
 main/como/rnaseq_preprocess.py | 150 +++++++++++++++++++++++----------
 1 file changed, 107 insertions(+), 43 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index ed598adc..36ebe264 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -17,7 +17,7 @@
 from loguru import logger
 
 from como.data_types import PATH_TYPE, LogLevel, RNAType
-from como.utils import _listify, _read_file, _set_up_logging
+from como.utils import _listify, _log_and_raise_error, _read_file, _set_up_logging
 
 
 @dataclass
@@ -36,7 +36,11 @@ def num_genes(self) -> int:
     @classmethod
     async def build_from_tab(cls, filepath: Path) -> _STARinformation:
         if filepath.suffix != ".tab":
-            raise ValueError(f"Building STAR information requires a '.tab' file; received: '{filepath}'")
+            _log_and_raise_error(
+                f"Building STAR information requires a '.tab' file; received: '{filepath}'",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         async with aiofiles.open(filepath) as i_stream:
             unmapped = await i_stream.readline()
@@ -90,25 +94,41 @@ def __post_init__(self):
         self.__sample_names = [f.stem for f in self.count_files]
 
         if len(self.count_files) != len(self.strand_files):
-            raise ValueError(
-                f"Unequal number of count files and strand files for study '{self.study_name}'. "
-                f"Found {len(self.count_files)} count files and {len(self.strand_files)} strand files."
+            _log_and_raise_error(
+                (
+                    f"Unequal number of count files and strand files for study '{self.study_name}'. "
+                    f"Found {len(self.count_files)} count files and {len(self.strand_files)} strand files."
+                ),
+                error=ValueError,
+                level=LogLevel.ERROR,
             )
 
         if self.num_samples != len(self.count_files):
-            raise ValueError(
-                f"Unequal number of samples and count files for study '{self.study_name}'. "
-                f"Found {self.num_samples} samples and {len(self.count_files)} count files."
+            _log_and_raise_error(
+                (
+                    f"Unequal number of samples and count files for study '{self.study_name}'. "
+                    f"Found {self.num_samples} samples and {len(self.count_files)} count files."
+                ),
+                error=ValueError,
+                level=LogLevel.ERROR,
             )
 
         if self.num_samples != len(self.strand_files):
-            raise ValueError(
-                f"Unequal number of samples and strand files for study '{self.study_name}'. "
-                f"Found {self.num_samples} samples and {len(self.strand_files)} strand files."
+            _log_and_raise_error(
+                (
+                    f"Unequal number of samples and strand files for study '{self.study_name}'. "
+                    f"Found {self.num_samples} samples and {len(self.strand_files)} strand files."
+                ),
+                error=ValueError,
+                level=LogLevel.ERROR,
             )
 
         if self.__num_samples == 1:
-            raise ValueError(f"Only one sample exists for study {self.study_name}. Provide at least two samples")
+            _log_and_raise_error(
+                f"Only one sample exists for study {self.study_name}. Provide at least two samples",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         self.count_files.sort()
         self.strand_files.sort()
@@ -127,10 +147,14 @@ def _organize_gene_counts_files(data_dir: Path) -> list[_StudyMetrics]:
     strandedness_directories: list[Path] = sorted([p for p in strand_dir.glob("*") if not p.name.startswith(".")])
 
     if len(gene_counts_directories) != len(strandedness_directories):
-        raise ValueError(
-            f"Unequal number of gene count directories and strandedness directories. "
-            f"Found {len(gene_counts_directories)} gene count directories and {len(strandedness_directories)} strandedness directories."  # noqa: E501
-            f"\nGene count directory: {gene_count_dir}\nStrandedness directory: {strand_dir}"
+        _log_and_raise_error(
+            (
+                f"Unequal number of gene count directories and strandedness directories. "
+                f"Found {len(gene_counts_directories)} gene count directories and {len(strandedness_directories)} strandedness directories."  # noqa: E501
+                f"\nGene count directory: {gene_count_dir}\nStrandedness directory: {strand_dir}"
+            ),
+            error=ValueError,
+            level=LogLevel.ERROR,
         )
 
     # For each study, collect gene count files, fragment files, insert size files, layouts, and strandedness information
@@ -139,9 +163,15 @@ def _organize_gene_counts_files(data_dir: Path) -> list[_StudyMetrics]:
         count_files = list(gene_dir.glob("*.tab"))
         strand_files = list(strand_dir.glob("*.txt"))
         if len(count_files) == 0:
-            raise ValueError(f"No count files found for study '{gene_dir.stem}'.")
+            _log_and_raise_error(
+                f"No count files found for study '{gene_dir.stem}'.", error=ValueError, level=LogLevel.ERROR
+            )
         if len(strand_files) == 0:
-            raise ValueError(f"No strandedness files found for study '{gene_dir.stem}'.")
+            _log_and_raise_error(
+                f"No strandedness files found for study '{gene_dir.stem}'.",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         study_metrics.append(
             _StudyMetrics(
@@ -160,9 +190,13 @@ async def _process_first_multirun_sample(strand_file: Path, all_counts_files: li
         strand_information = strand_file.read_text().rstrip("\n").lower()
 
         if strand_information not in ("none", "first_read_transcription_strand", "second_read_transcription_strand"):
-            raise ValueError(
-                f"Unrecognized Strand Information: {strand_information}; "
-                f"expected 'none', 'first_read_transcription_strand', or 'second_read_transcription_strand'"
+            _log_and_raise_error(
+                (
+                    f"Unrecognized Strand Information: {strand_information}; "
+                    f"expected 'none', 'first_read_transcription_strand', or 'second_read_transcription_strand'"
+                ),
+                error=ValueError,
+                level=LogLevel.ERROR,
             )
 
         if strand_information == "none":
@@ -191,9 +225,13 @@ async def _process_standard_replicate(counts_file: Path, strand_file: Path, samp
     strand_information = strand_file.read_text().rstrip("\n").lower()
 
     if strand_information not in ("none", "first_read_transcription_strand", "second_read_transcription_strand"):
-        raise ValueError(
-            f"Unrecognized Strand Information: {strand_information}; "
-            f"expected 'none', 'first_read_transcription_strand', or 'second_read_transcription_strand'"
+        _log_and_raise_error(
+            (
+                f"Unrecognized Strand Information: {strand_information}; "
+                f"expected 'none', 'first_read_transcription_strand', or 'second_read_transcription_strand'"
+            ),
+            error=ValueError,
+            level=LogLevel.ERROR,
         )
 
     if strand_information == "none":
@@ -306,7 +344,9 @@ async def _create_config_df(  # noqa: C901
     preparation_method: list[str] = []
 
     if len(gene_counts_files) == 0:
-        raise FileNotFoundError(f"No gene count files found in '{gene_counts_dir}'.")
+        _log_and_raise_error(
+            f"No gene count files found in '{gene_counts_dir}'.", error=FileNotFoundError, level=LogLevel.ERROR
+        )
 
     for gene_count_filename in sorted(gene_counts_files):
         # Match S___R___r___
@@ -314,11 +354,15 @@ async def _create_config_df(  # noqa: C901
         # (?:r\d{1,3})? optionally matches a "r" followed by three digits
         label = re.findall(r"S\d{1,3}R\d{1,3}(?:r\d{1,3})?", gene_count_filename.as_posix())[0]
         if not label:
-            raise ValueError(
-                f"\n\nFilename of '{gene_count_filename}' is not valid. "
-                f"Should be 'contextName_SXRYrZ.tab', where X is the study/batch number, Y is the replicate number, "
-                f"and Z is the run number."
-                "\n\nIf not a multi-run sample, exclude 'rZ' from the filename."
+            _log_and_raise_error(
+                (
+                    f"\n\nFilename of '{gene_count_filename}' is not valid. "
+                    f"Should be 'contextName_SXRYrZ.tab', where X is the study/batch number, Y is the replicate number, "
+                    f"and Z is the run number."
+                    "\n\nIf not a multi-run sample, exclude 'rZ' from the filename."
+                ),
+                error=ValueError,
+                level=LogLevel.ERROR,
             )
 
         study_number = re.findall(r"S\d{1,3}", label)[0]
@@ -355,9 +399,13 @@ async def _create_config_df(  # noqa: C901
             with layout_files[0].open("r") as file:
                 layout = file.read().strip()
         elif len(layout_files) > 1:
-            raise ValueError(
-                f"Multiple matching layout files for {label}, "
-                f"make sure there is only one copy for each replicate in COMO_input"
+            _log_and_raise_error(
+                (
+                    f"Multiple matching layout files for {label}, "
+                    f"make sure there is only one copy for each replicate in COMO_input"
+                ),
+                error=ValueError,
+                level=LogLevel.ERROR,
             )
 
         strand = "UNKNOWN"
@@ -371,9 +419,13 @@ async def _create_config_df(  # noqa: C901
             with strand_files[0].open("r") as file:
                 strand = file.read().strip()
         elif len(strand_files) > 1:
-            raise ValueError(
-                f"Multiple matching strandedness files for {label}, "
-                f"make sure there is only one copy for each replicate in COMO_input"
+            _log_and_raise_error(
+                (
+                    f"Multiple matching strandedness files for {label}, "
+                    f"make sure there is only one copy for each replicate in COMO_input"
+                ),
+                error=ValueError,
+                level=LogLevel.ERROR,
             )
 
         prep = "total"
@@ -383,11 +435,19 @@ async def _create_config_df(  # noqa: C901
             with prep_files[0].open("r") as file:
                 prep = file.read().strip().lower()
                 if prep not in ["total", "mrna"]:
-                    raise ValueError(f"Prep method must be either 'total' or 'mrna' for {label}")
+                    _log_and_raise_error(
+                        f"Prep method must be either 'total' or 'mrna' for {label}",
+                        error=ValueError,
+                        level=LogLevel.ERROR,
+                    )
         elif len(prep_files) > 1:
-            raise ValueError(
-                f"Multiple matching prep files for {label}, "
-                f"make sure there is only one copy for each replicate in COMO_input"
+            _log_and_raise_error(
+                (
+                    f"Multiple matching prep files for {label}, "
+                    f"make sure there is only one copy for each replicate in COMO_input"
+                ),
+                error=ValueError,
+                level=LogLevel.ERROR,
             )
 
         mean_fragment_size = 100
@@ -417,9 +477,13 @@ async def _create_config_df(  # noqa: C901
 
                     mean_fragment_size = sum(mean_fragment_sizes * library_sizes) / sum(library_sizes)
         elif len(frag_files) > 1:
-            raise ValueError(
-                f"Multiple matching fragment files for {label}, "
-                f"make sure there is only one copy for each replicate in COMO_input"
+            _log_and_raise_error(
+                (
+                    f"Multiple matching fragment files for {label}, "
+                    f"make sure there is only one copy for each replicate in COMO_input"
+                ),
+                error=ValueError,
+                level=LogLevel.ERROR,
             )
 
         sample_names.append(f"{context_name}_{study_number}{rep_number}")

From 4d66cd38288afec517be45869dd8783e47ba13b0 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:19:10 -0600
Subject: [PATCH 143/345] feat: set appropriate logging

---
 main/como/rnaseq_preprocess.py | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 36ebe264..d2b5c634 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -667,13 +667,7 @@ async def rnaseq_preprocess(
     :param log_level: The logging level
     :param log_location: The logging location
     """
-    with contextlib.suppress(ValueError):
-        logger.remove(0)
-        logger.add(
-            sink=log_location,
-            level=log_level,
-            format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>",  # noqa: E501
-        )
+    _set_up_logging(level=log_level, location=log_location)
 
     output_gene_info_filepath = output_gene_info_filepath.resolve()
     como_context_dir = como_context_dir.resolve()

From 40cdfd47c3278db62295b2ffd6e6c5908838462c Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:19:51 -0600
Subject: [PATCH 144/345] feat: combine lines async file reading

---
 main/como/rnaseq_preprocess.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index d2b5c634..a34b7abd 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -54,8 +54,7 @@ async def build_from_tab(cls, filepath: Path) -> _STARinformation:
             num_ambiguous = [int(i) for i in ambiguous.rstrip("\n").split("\t")[1:]]
             remainder = await i_stream.read()
 
-        string_io = StringIO(remainder)
-        df = pd.read_csv(string_io, sep="\t", header=None)
+        df = await _read_file(StringIO(remainder), sep="\t", header=None)
         df.columns = [
             "ensembl_gene_id",
             "unstranded_rna_counts",

From 509ed961dc0768b6f5980fe5a12563b6c1f1f55d Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:20:39 -0600
Subject: [PATCH 145/345] feat: create Enum from rna types

---
 main/como/data_types.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/main/como/data_types.py b/main/como/data_types.py
index a1cbc5b6..0c5d00bc 100644
--- a/main/como/data_types.py
+++ b/main/como/data_types.py
@@ -68,6 +68,12 @@ def from_string(value: str) -> FilteringTechnique:
 RNA_TYPE = Literal["total", "mrna"]
 LOG_LEVEL = Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]
 
+class RNAType(Enum):
+    TRNA = "total"
+    MRNA = "mrna"
+    SCRNA = "scrna"
+
+
 class CobraCompartments:
     """Convert from compartment "long-hand" to "short-hand".
 

From 55c04d1a54a5a5484a3f8240cd146a9c19bbe627 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:21:27 -0600
Subject: [PATCH 146/345] fix: use new RNA type enum

---
 main/como/rnaseq_preprocess.py | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index a34b7abd..718caa11 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -292,7 +292,7 @@ async def _write_counts_matrix(
     config_df: pd.DataFrame,
     como_context_dir: Path,
     output_counts_matrix_filepath: Path,
-    rna: RNA_TYPE,
+    rna: RNAType,
 ) -> pd.DataFrame:
     """Create a counts matrix file by reading gene counts table(s)."""
     study_metrics = _organize_gene_counts_files(data_dir=como_context_dir)
@@ -304,12 +304,12 @@ async def _write_counts_matrix(
     for count in counts:
         final_matrix = count if final_matrix.empty else pd.merge(final_matrix, count, on="ensembl_gene_id", how="outer")
 
-    rna_specific_sample_names = config_df.loc[config_df["library_prep"] == rna, "sample_name"].tolist()
+    rna_specific_sample_names = config_df.loc[config_df["library_prep"] == rna.value, "sample_name"].tolist()
     final_matrix = final_matrix[["ensembl_gene_id", *rna_specific_sample_names]]
 
     output_counts_matrix_filepath.parent.mkdir(parents=True, exist_ok=True)
     final_matrix.to_csv(output_counts_matrix_filepath, index=False)
-    logger.success(f"Wrote gene count matrix for '{rna}' RNA at '{output_counts_matrix_filepath}'")
+    logger.success(f"Wrote gene count matrix for '{rna.value}' RNA at '{output_counts_matrix_filepath}'")
     return final_matrix
 
 
@@ -450,7 +450,7 @@ async def _create_config_df(  # noqa: C901
             )
 
         mean_fragment_size = 100
-        if len(frag_files) == 0 and prep != RNAPrepMethod.TOTAL.value:
+        if len(frag_files) == 0 and prep != RNAType.TRNA.value:
             logger.warning(
                 f"No fragment file found for {label}, using '100'. "
                 "You should define this if you are going to use downstream zFPKM normalization"
@@ -571,7 +571,7 @@ async def _create_matrix_file(
     output_config_filepath: Path,
     como_context_dir: PATH_TYPE,
     output_counts_matrix_filepath: Path,
-    rna: RNA_TYPE,
+    rna: RNAType,
 ) -> None:
     config_df = await _create_config_df(context_name, como_context_dir=como_context_dir)
     await _write_counts_matrix(
@@ -581,7 +581,7 @@ async def _create_matrix_file(
         rna=rna,
     )
     with pd.ExcelWriter(output_config_filepath) as writer:
-        subset_config = config_df[config_df["library_prep"] == rna]
+        subset_config = config_df[config_df["library_prep"] == rna.value]
         subset_config.to_excel(writer, sheet_name=context_name, header=True, index=False)
 
 
@@ -597,11 +597,11 @@ async def _process(
     output_mrna_matrix_filepath: Path | None,
     cache: bool,
 ):
-    rna_types: list[tuple[RNA_TYPE, Path, Path]] = []
+    rna_types: list[tuple[RNAType, Path, Path]] = []
     if output_trna_config_filepath:
-        rna_types.append(("total", output_trna_config_filepath, output_trna_matrix_filepath))
+        rna_types.append((RNAType.trna, output_trna_config_filepath, output_trna_matrix_filepath))
     if output_mrna_config_filepath:
-        rna_types.append(("mrna", output_mrna_config_filepath, output_mrna_matrix_filepath))
+        rna_types.append((RNAType.mrna, output_mrna_config_filepath, output_mrna_matrix_filepath))
 
     # if provided, iterate through como-input specific directories
     tasks = []

From d7ecd509f181477604baa82b972bca1c69bed445 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:22:28 -0600
Subject: [PATCH 147/345] refactor: convert literals to Enums

---
 main/como/data_types.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/main/como/data_types.py b/main/como/data_types.py
index 0c5d00bc..56e91ef1 100644
--- a/main/como/data_types.py
+++ b/main/como/data_types.py
@@ -65,8 +65,16 @@ def from_string(value: str) -> FilteringTechnique:
 
 
 PATH_TYPE = str | Path
-RNA_TYPE = Literal["total", "mrna"]
-LOG_LEVEL = Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]
+
+class LogLevel(Enum):
+    TRACE = 5
+    DEBUG = 10
+    INFO = 20
+    SUCCESS = 25
+    WARNING = 30
+    ERROR = 40
+    CRITICAL = 50
+
 
 class RNAType(Enum):
     TRNA = "total"

From c69fd1a5793f524fd89b4905b07ba6d4f6acdd19 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:23:17 -0600
Subject: [PATCH 148/345] fix: re-organize code

---
 main/como/data_types.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/main/como/data_types.py b/main/como/data_types.py
index 56e91ef1..71cf8668 100644
--- a/main/como/data_types.py
+++ b/main/como/data_types.py
@@ -64,7 +64,6 @@ def from_string(value: str) -> FilteringTechnique:
                 raise ValueError(f"Got a filtering technique of '{value}'; should be one of: {possible_values}")
 
 
-PATH_TYPE = str | Path
 
 class LogLevel(Enum):
     TRACE = 5

From 189cfe5c17318f18807209ad01bfaf38961e4d97 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:28:02 -0600
Subject: [PATCH 149/345] fix: added dataclasses for simplification of data

---
 main/como/data_types.py | 175 +++++++++++++++++++++++++++++++---------
 1 file changed, 135 insertions(+), 40 deletions(-)

diff --git a/main/como/data_types.py b/main/como/data_types.py
index 71cf8668..e4e7d8b2 100644
--- a/main/como/data_types.py
+++ b/main/como/data_types.py
@@ -1,8 +1,13 @@
 from __future__ import annotations
 
+from collections.abc import Iterator
+from dataclasses import dataclass, field, fields
 from enum import Enum
 from pathlib import Path
-from typing import Literal
+from typing import ClassVar, NamedTuple
+
+import pandas as pd
+from loguru import logger
 
 PATH_TYPE = str | Path
 LOG_FORMAT = (
@@ -12,26 +17,15 @@
 )
 
 
-class RNAPrepMethod(Enum):
-    TOTAL = "total"
-    MRNA = "mrna"
-    SCRNA = "scrna"
+class AdjustmentMethod(Enum):
+    """Adjustment method for expression requirement based on differences in number of provided data source types."""
+
+    PROGRESSIVE = "progressive"
+    REGRESSIVE = "regressive"
+    FLAT = "flat"
+    CUSTOM = "custom"
+
 
-    @staticmethod
-    def from_string(value: str) -> RNAPrepMethod:
-        """Build a preparation method object from a string."""
-        match_value = "".join(c for c in value if c.isascii()).lower()
-
-        match match_value:
-            case "total" | "trna":
-                return RNAPrepMethod.TOTAL
-            case "mrna":
-                return RNAPrepMethod.MRNA
-            case "scrna":
-                return RNAPrepMethod.SCRNA
-            case _:
-                possible_values = [t.value for t in RNAPrepMethod]
-                raise ValueError(f"Filtering technique must be one of {possible_values}; got: {value}")
 class Algorithm(Enum):
     GIMME = "GIMME"
     FASTCORE = "FASTCORE"
@@ -42,28 +36,17 @@ class Algorithm(Enum):
 class FilteringTechnique(Enum):
     """RNA sequencing filtering capabilities."""
 
-    cpm = "cpm"
-    zfpkm = "zfpkm"
-    tpm = "quantile"
-    umi = "umi"
-
-    @staticmethod
-    def from_string(value: str) -> FilteringTechnique:
-        """Create a filtering technique object from a string."""
-        match value.lower():
-            case "cpm":
-                return FilteringTechnique.cpm
-            case "zfpkm":
-                return FilteringTechnique.zfpkm
-            case "quantile":
-                return FilteringTechnique.tpm
-            case "umi":
-                return FilteringTechnique.umi
-            case _:
-                possible_values = [t.value for t in FilteringTechnique]
-                raise ValueError(f"Got a filtering technique of '{value}'; should be one of: {possible_values}")
+    CPM = "cpm"
+    ZFPKM = "zfpkm"
+    TPM = "quantile"
+    UMI = "umi"
 
 
+class GeneIdentifier(Enum):
+    ENSEMBL_GENE_ID = "ensembl_gene_id"
+    ENTREZ_GENE_ID = "entrez_gene_id"
+    GENE_SYMBOL = "gene_symbol"
+
 
 class LogLevel(Enum):
     TRACE = 5
@@ -75,12 +58,33 @@ class LogLevel(Enum):
     CRITICAL = 50
 
 
+class PeakIdentificationParameters(NamedTuple):
+    height: float
+    distance: float
+
+
 class RNAType(Enum):
     TRNA = "total"
     MRNA = "mrna"
     SCRNA = "scrna"
 
 
+class Solver(Enum):
+    """Solver used to seed context specific model."""
+
+    GLPK = "GLPK"
+    GUROBI = "GUROBI"
+    SCIPY = "SCIPY"
+    GLPK_EXACT = "GLPK_EXACT"
+
+
+class SourceTypes(Enum):
+    TRNA = "trna"
+    MRNA = "mrna"
+    SCRNA = "scrna"
+    PROTEOMICS = "proteomics"
+
+
 class CobraCompartments:
     """Convert from compartment "long-hand" to "short-hand".
 
@@ -148,3 +152,94 @@ def get_longhand(cls, shorthand: str) -> str | None:
         longhand = cls.SHORTHAND.get(shorthand.lower(), None)
         return longhand[0] if longhand else None
 
+
+@dataclass
+class _BatchEntry:
+    batch_num: int
+    sample_names: list[str]
+    _num_samples: int = field(init=False)
+
+    def __post_init__(self):
+        self._num_samples = len(self.sample_names)
+
+    @property
+    def num_samples(self):
+        return self._num_samples
+
+
+@dataclass
+class _CombineOmicsInput:
+    z_score_matrix: pd.DataFrame
+    type: SourceTypes
+    weight: int
+
+
+class _BaseDataType:
+    """Base class for common data types."""
+
+    def __getitem__(self, value: str):
+        """Access matrices using square bracket notation (e.g., `input_matrices['total_rna']`).
+
+        :param value: The name of the matrix to get ('trna', 'mrna', 'scrna', 'proteomics')
+        :returns: The DataFrame if it exists, None otherwise.
+        """
+        self._validate_attribute(value)
+        return getattr(self, value)
+
+    def __setitem__(self, key, value):
+        """Set matrices using square bracket notation (e.g., `input_matrices['total_rna'] = new_df`).
+
+        :param key: The key to set
+        :param value: The new value
+        """
+        self._validate_attribute(key)
+        setattr(self, key, value)
+
+    def _validate_attribute(self, key):
+        if key not in {i.value for i in SourceTypes._member_map_.values()}:
+            # Unable to use como.utils._log_and_raise_error becuase it results in a circular import
+            message = f"{key} is not a valid attribute of {SourceTypes.__name__}; got '{key}'"
+            logger.warning(message)
+            raise ValueError(message)
+
+    def __iter__(self) -> Iterator[tuple[SourceTypes, pd.DataFrame | None]]:
+        """Iterate over matrix fields and their names.
+
+        Yields:
+            A tuple containing (matrix_name, matrix_dataframe).
+
+        """
+        for field_ in fields(self):
+            yield SourceTypes(field_.name), getattr(self, field_.name)
+
+
+@dataclass
+class _BatchNames(_BaseDataType):
+    trna: list[_BatchEntry]
+    mrna: list[_BatchEntry]
+    scrna: list[_BatchEntry]
+    proteomics: list[_BatchEntry]
+
+
+@dataclass
+class _InputMatrices(_BaseDataType):
+    trna: pd.DataFrame | None = None
+    mrna: pd.DataFrame | None = None
+    scrna: pd.DataFrame | None = None
+    proteomics: pd.DataFrame | None = None
+
+
+@dataclass
+class _OutputCombinedSourceFilepath(_BaseDataType):
+    trna: Path | None
+    mrna: Path | None
+    scrna: Path | None
+    proteomics: Path | None
+
+
+@dataclass
+class _SourceWeights(_BaseDataType):
+    trna: int
+    mrna: int
+    scrna: int
+    proteomics: int

From 4008dc29283e06c12fcde313938512d3ccd45390 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:32:29 -0600
Subject: [PATCH 150/345] feat: use _log_and_raise_error  function

---
 main/como/cluster_rnaseq.py | 61 ++++++++++++++++++++++++++++++-------
 1 file changed, 50 insertions(+), 11 deletions(-)

diff --git a/main/como/cluster_rnaseq.py b/main/como/cluster_rnaseq.py
index dba5499c..818a45bc 100644
--- a/main/como/cluster_rnaseq.py
+++ b/main/como/cluster_rnaseq.py
@@ -7,7 +7,8 @@
 
 import numpy as np
 
-from como.utils import stringlist_to_list
+from como.data_types import LogLevel
+from como.utils import _log_and_raise_error, stringlist_to_list
 
 
 @dataclass
@@ -34,43 +35,81 @@ def __post_init__(self):  # noqa: C901, ignore too complex
             self.seed = np.random.randint(0, 100_000)
 
         if (isdigit(self.min_active_count) and int(self.min_active_count) < 0) or self.min_active_count != "default":
-            raise ValueError("--min-count must be either 'default' or an integer > 0")
+            _log_and_raise_error(
+                "min_active_count must be either 'default' or an integer > 0", error=ValueError, level=LogLevel.ERROR
+            )
 
         if (isdigit(self.quantile) and 0 > int(self.quantile) > 100) or self.quantile != "default":
-            raise ValueError("--quantile must be either 'default' or an integer between 0 and 100")
+            _log_and_raise_error(
+                "quantile must be either 'default' or an integer between 0 and 100",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         if (isdigit(self.replicate_ratio) and 0 > self.replicate_ratio > 1.0) or self.replicate_ratio != "default":
-            raise ValueError("--rep-ratio must be either 'default' or a float between 0 and 1")
+            _log_and_raise_error(
+                "--rep-ratio must be either 'default' or a float between 0 and 1",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         if (isdigit(self.batch_ratio) and 0 > self.batch_ratio > 1.0) or self.batch_ratio != "default":
-            raise ValueError("--batch-ratio must be either 'default' or a float between 0 and 1")
+            _log_and_raise_error(
+                "--batch-ratio must be either 'default' or a float between 0 and 1",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         if self.filtering_technique.lower() not in {"quantile", "tpm", "cpm", "zfpkm"}:
-            raise ValueError("--technique must be either 'quantile', 'tpm', 'cpm', 'zfpkm'")
+            _log_and_raise_error(
+                "--technique must be either 'quantile', 'tpm', 'cpm', 'zfpkm'",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         if self.filtering_technique.lower() == "tpm":
             self.filtering_technique = "quantile"
 
         if self.cluster_algorithm.lower() not in {"mca", "umap"}:
-            raise ValueError("--clust_algo must be either 'mca', 'umap'")
+            _log_and_raise_error(
+                "--clust_algo must be either 'mca', 'umap'",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         if 0 > self.min_distance > 1.0:
-            raise ValueError("--min_dist must be a float between 0 and 1")
+            _log_and_raise_error(
+                "--min_dist must be a float between 0 and 1",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         if (
             isdigit(self.num_replicate_neighbors) and self.num_replicate_neighbors < 1
         ) or self.num_replicate_neighbors != "default":
-            raise ValueError("--n-neighbors-rep must be either 'default' or an integer > 1")
+            _log_and_raise_error(
+                "--n-neighbors-rep must be either 'default' or an integer > 1",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         if (
             isdigit(self.num_batch_neighbors) and self.num_batch_neighbors < 1
         ) or self.num_batch_neighbors != "default":
-            raise ValueError("--n-neighbors-batch must be either 'default' or an integer > 1")
+            _log_and_raise_error(
+                "--n-neighbors-batch must be either 'default' or an integer > 1",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         if (
             isdigit(self.num_context_neighbors) and self.num_context_neighbors < 1
         ) or self.num_context_neighbors != "default":
-            raise ValueError("--n-neighbors-context must be either 'default' or an integer > 1")
+            _log_and_raise_error(
+                "--n-neighbors-context must be either 'default' or an integer > 1",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
 
 def _parse_args() -> _Arguments:

From 111465d2aae05e6d6db05b5b754f1cf7157e2af2 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:32:44 -0600
Subject: [PATCH 151/345] fix: drop na values

---
 main/como/rnaseq_preprocess.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 718caa11..4ae690b6 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -308,6 +308,7 @@ async def _write_counts_matrix(
     final_matrix = final_matrix[["ensembl_gene_id", *rna_specific_sample_names]]
 
     output_counts_matrix_filepath.parent.mkdir(parents=True, exist_ok=True)
+    final_matrix.dropna(inplace=True)
     final_matrix.to_csv(output_counts_matrix_filepath, index=False)
     logger.success(f"Wrote gene count matrix for '{rna.value}' RNA at '{output_counts_matrix_filepath}'")
     return final_matrix

From 14fb0fac3c58ac02b6ca0e7c7592a10658e66501 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:33:32 -0600
Subject: [PATCH 152/345] feat: async read matrix files

---
 main/como/rnaseq_preprocess.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 4ae690b6..442eef1b 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -519,13 +519,15 @@ async def _create_gene_info_file(
     """
 
     async def read_counts(file: Path) -> list[str]:
-        data = await asyncio.to_thread(pd.read_csv if file.suffix == ".csv" else sc.read_h5ad, file)
-        conversion = (
-            await ensembl_to_gene_id_and_symbol(ids=data["ensembl_gene_id"].tolist(), taxon=taxon)
+        data = await _read_file(file, h5ad_as_df=False)
+        conversion = await (
+            ensembl_to_gene_id_and_symbol(ids=data["ensembl_gene_id"].tolist(), taxon=taxon)
             if isinstance(data, pd.DataFrame)
-            else await gene_symbol_to_ensembl_and_gene_id(symbols=data.var_names.tolist(), taxon=taxon)
+            else gene_symbol_to_ensembl_and_gene_id(symbols=data.var_names.tolist(), taxon=taxon)
         )
-        return conversion["entrez_gene_id"].tolist()
+
+        # Remove NA values from entrez_gene_id dataframe column
+        return conversion["entrez_gene_id"].dropna().tolist()
 
     logger.info(
         "Fetching gene info (this may take 1-5 minutes depending on the number of genes and your internet connection)"

From f6326791d5c4c7d335cc0da934d4db018f834c46 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:34:12 -0600
Subject: [PATCH 153/345] refactor: calculate size more pythonically

---
 main/como/rnaseq_preprocess.py | 11 ++++-------
 1 file changed, 4 insertions(+), 7 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 442eef1b..1f46b755 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -536,7 +536,7 @@ async def read_counts(file: Path) -> list[str]:
     gene_data = await MyGene(cache=cache).query(items=list(genes), taxon=taxon, scopes="entrezgene")
     gene_info: pd.DataFrame = pd.DataFrame(
         data=None,
-        columns=pd.Index(data=["ensembl_gene_id", "gene_symbol", "entrez_gene_id", "start_position", "end_position"]),
+        columns=pd.Index(data=["ensembl_gene_id", "gene_symbol", "entrez_gene_id", "size"]),
         index=pd.Index(data=range(len(gene_data))),
     )
     for i, data in enumerate(gene_data):
@@ -545,15 +545,14 @@ async def read_counts(file: Path) -> list[str]:
             ensembl_ids = ensembl_ids[0]
 
         start_pos = data.get("genomic_pos.start", 0)
-        start_pos = sum(start_pos) / len(start_pos) if isinstance(start_pos, list) else start_pos
+        start_pos: int = int(sum(start_pos) / len(start_pos)) if isinstance(start_pos, list) else int(start_pos)
         end_pos = data.get("genomic_pos.end", 0)
-        end_pos = sum(end_pos) / len(end_pos) if isinstance(end_pos, list) else end_pos
+        end_pos: int = int(sum(end_pos) / len(end_pos)) if isinstance(end_pos, list) else int(end_pos)
 
         gene_info.at[i, "gene_symbol"] = data.get("symbol", "-")
         gene_info.at[i, "entrez_gene_id"] = data.get("entrezgene", "-")
         gene_info.at[i, "ensembl_gene_id"] = ensembl_ids
-        gene_info.at[i, "start_position"] = start_pos
-        gene_info.at[i, "end_position"] = end_pos
+        gene_info.at[i, "size"] = end_pos - start_pos
 
     gene_info = gene_info[
         (
@@ -562,8 +561,6 @@ async def read_counts(file: Path) -> list[str]:
             & (gene_info["gene_symbol"] != "-")
         )
     ]
-    gene_info["size"] = gene_info["end_position"].astype(int) - gene_info["start_position"].astype(int)
-    gene_info.drop(columns=["start_position", "end_position"], inplace=True)
     gene_info.sort_values(by="ensembl_gene_id", inplace=True)
     gene_info.to_csv(output_filepath, index=False)
     logger.success(f"Gene Info file written at '{output_filepath}'")

From 569061679764d0a3fc4166503ad6a9c2103d6757 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:34:37 -0600
Subject: [PATCH 154/345] fix: use better variable for ensembl gene

---
 main/como/rnaseq_preprocess.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 1f46b755..5f778fb7 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -540,7 +540,7 @@ async def read_counts(file: Path) -> list[str]:
         index=pd.Index(data=range(len(gene_data))),
     )
     for i, data in enumerate(gene_data):
-        ensembl_ids = data.get("ensembl.gene", "-")
+        ensembl_ids = data.get("genomic_pos.ensemblgene", "-")
         if isinstance(ensembl_ids, list):
             ensembl_ids = ensembl_ids[0]
 

From 746099bfc8573ec232e611a0cfc9d304e62c5792 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:34:45 -0600
Subject: [PATCH 155/345] refactor: drop na values

---
 main/como/rnaseq_preprocess.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 5f778fb7..9edfb67d 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -562,6 +562,7 @@ async def read_counts(file: Path) -> list[str]:
         )
     ]
     gene_info.sort_values(by="ensembl_gene_id", inplace=True)
+    gene_info.dropna(inplace=True)
     gene_info.to_csv(output_filepath, index=False)
     logger.success(f"Gene Info file written at '{output_filepath}'")
 

From 132660f5585a8044aad3d63bfb7169aacf67da0d Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:34:54 -0600
Subject: [PATCH 156/345] chore: fix logging message

---
 main/como/rnaseq_preprocess.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 9edfb67d..7c076162 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -530,7 +530,8 @@ async def read_counts(file: Path) -> list[str]:
         return conversion["entrez_gene_id"].dropna().tolist()
 
     logger.info(
-        "Fetching gene info (this may take 1-5 minutes depending on the number of genes and your internet connection)"
+        "Fetching gene info - "
+        "this can take up to 5 minutes depending on the number of genes and your internet connection"
     )
     genes = set(chain.from_iterable(await asyncio.gather(*[read_counts(f) for f in counts_matrix_filepaths])))
     gene_data = await MyGene(cache=cache).query(items=list(genes), taxon=taxon, scopes="entrezgene")

From 1f7d2529a9e56be60df388a4523e9f63363ea3d8 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:35:09 -0600
Subject: [PATCH 157/345] feat: use new LogLevel Enum

---
 main/como/rnaseq_preprocess.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 7c076162..f4b4d14c 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -646,7 +646,7 @@ async def rnaseq_preprocess(
     output_trna_count_matrix_filepath: Path | None = None,
     output_mrna_count_matrix_filepath: Path | None = None,
     cache: bool = True,
-    log_level: LOG_LEVEL = "INFO",
+    log_level: LogLevel = LogLevel.INFO,
     log_location: str | TextIOWrapper = sys.stderr,
 ) -> None:
     """Preprocesses RNA-seq data for downstream analysis.

From 7451732aa438425e6223eec6da739a6905ba92cf Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:35:23 -0600
Subject: [PATCH 158/345] fix: resolve context directory only if provided

---
 main/como/rnaseq_preprocess.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index f4b4d14c..0846d6d2 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -671,7 +671,9 @@ async def rnaseq_preprocess(
     _set_up_logging(level=log_level, location=log_location)
 
     output_gene_info_filepath = output_gene_info_filepath.resolve()
-    como_context_dir = como_context_dir.resolve()
+
+    if como_context_dir:
+        como_context_dir = como_context_dir.resolve()
     input_matrix_filepath = [i.resolve() for i in _listify(input_matrix_filepath)] if input_matrix_filepath else None
     output_trna_metadata_filepath = (
         output_trna_metadata_filepath.resolve() if output_trna_metadata_filepath else output_trna_metadata_filepath

From 58b66e9f84d0a9737cab7c2fce43306544047c42 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:35:44 -0600
Subject: [PATCH 159/345] refactor: import/remove modules

---
 main/como/rnaseq_gen.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index b8a2f735..9fc6d9e1 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -1,13 +1,13 @@
 from __future__ import annotations
 
-import math
 import multiprocessing
+import sys
 import time
 from collections import namedtuple
+from concurrent.futures import Future, ProcessPoolExecutor, as_completed
 from dataclasses import dataclass, field
 from enum import Enum
-from functools import partial
-from multiprocessing.pool import Pool
+from io import TextIOWrapper
 from pathlib import Path
 from typing import Callable, NamedTuple
 
@@ -18,16 +18,17 @@
 import scanpy as sc
 import sklearn
 import sklearn.neighbors
-from fast_bioservices.pipeline import ensembl_to_gene_id_and_symbol
+from fast_bioservices.pipeline import ensembl_to_gene_id_and_symbol, gene_symbol_to_ensembl_and_gene_id
 from loguru import logger
 from pandas import DataFrame
 from plotly.subplots import make_subplots
 from scipy.signal import find_peaks
 from sklearn.neighbors import KernelDensity
 
-from como.data_types import FilteringTechnique, RNAPrepMethod
+from como.data_types import FilteringTechnique, LogLevel, PeakIdentificationParameters, RNAType
 from como.migrations import gene_info_migrations
 from como.project import Config
+from como.utils import _log_and_raise_error, _num_columns, _read_file, _set_up_logging
 
 
 class _FilteringOptions(NamedTuple):

From 5c7e3b2cde57baa560cb012b7a638f00d2205ab7 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:42:59 -0600
Subject: [PATCH 160/345] refactor: remove extra read_counts function

---
 main/como/rnaseq_gen.py | 28 ++++++++++------------------
 1 file changed, 10 insertions(+), 18 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 9fc6d9e1..e165bebd 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -63,7 +63,11 @@ class _StudyMetrics:
     def __post_init__(self):
         for layout in self.layout:
             if layout not in LayoutMethod:
-                raise ValueError(f"Layout must be 'paired-end' or 'single-end'; got: {layout}")
+                _log_and_raise_error(
+                    f"Layout must be 'paired-end' or 'single-end'; got: {layout}",
+                    error=ValueError,
+                    level=LogLevel.ERROR,
+                )
 
     @property
     def normalization_matrix(self) -> pd.DataFrame:
@@ -133,7 +137,11 @@ def genefilter(data: pd.DataFrame | npt.NDArray, filter_func: Callable[[npt.NDAr
     :return: A NumPy array of the filtered data.
     """
     if not isinstance(data, (pd.DataFrame, npt.NDArray)):
-        raise TypeError("Unsupported data type. Must be a Pandas DataFrame or a NumPy array.")
+        _log_and_raise_error(
+            f"Unsupported data type. Must be a Pandas DataFrame or a NumPy array, got '{type(data)}'",
+            error=TypeError,
+            level=LogLevel.CRITICAL,
+        )
 
     return (
         data.apply(filter_func, axis=1).values
@@ -142,22 +150,6 @@ def genefilter(data: pd.DataFrame | npt.NDArray, filter_func: Callable[[npt.NDAr
     )
 
 
-async def _read_counts(path: Path) -> pd.DataFrame:
-    if path.suffix not in {".csv", ".h5ad"}:
-        raise ValueError(f"Unknown file extension '{path.suffix}'. Valid options are '.csv' or '.h5ad'.")
-
-    matrix: pd.DataFrame
-    if path.suffix == ".csv":
-        logger.debug(f"Reading CSV file at '{path}'")
-        matrix = pd.read_csv(path, header=0)
-    elif path.suffix == ".h5ad":
-        logger.debug(f"Reading h5ad file at '{path}'")
-        # Make sample names the columns and gene data the index
-        matrix = sc.read_h5ad(path).to_df().T
-
-    return matrix
-
-
 async def _build_matrix_results(
     *,
     matrix: pd.DataFrame,

From 369b5a13899f54a9af3cb842e710f804648d9ebb Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:43:21 -0600
Subject: [PATCH 161/345] fix: drop na values when building compiling matrix

---
 main/como/rnaseq_gen.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index e165bebd..24e477c7 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -164,11 +164,12 @@ async def _build_matrix_results(
     :param taxon: The NCBI Taxon ID
     :return: A dataclass `ReadMatrixResults`
     """
+    matrix.dropna(inplace=True)
     conversion = await ensembl_to_gene_id_and_symbol(ids=matrix["ensembl_gene_id"].tolist(), taxon=taxon)
     conversion["ensembl_gene_id"] = conversion["ensembl_gene_id"].str.split(",")
     conversion = conversion.explode("ensembl_gene_id")
     conversion.reset_index(inplace=True, drop=True)
-    matrix = matrix.merge(conversion, on="ensembl_gene_id", how="left")
+    matrix = matrix.merge(conversion, on=["ensembl_gene_id", "entrez_gene_id", "gene_symbol"], how="left")
 
     # Only include Entrez and Ensembl Gene IDs that are present in `gene_info`
     matrix["entrez_gene_id"] = matrix["entrez_gene_id"].str.split("//")

From e708644cd8f2c06c7112f553be68487ab3044d88 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:43:47 -0600
Subject: [PATCH 162/345] fix: fpkm calculations

---
 main/como/rnaseq_gen.py | 44 ++++++++++++++++++++++++-----------------
 1 file changed, 26 insertions(+), 18 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 24e477c7..d0234ec3 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -233,38 +233,46 @@ def calculate_tpm(metrics: NamedMetrics) -> NamedMetrics:
     return metrics
 
 
-def _calculate_fpkm(metrics: NamedMetrics) -> NamedMetrics:
+def _calculate_fpkm(metrics: NamedMetrics, scale: int = 1e6) -> NamedMetrics:
     """Calculate the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) for each sample in the metrics dictionary."""  # noqa: E501
     for study in metrics:
         matrix_values = []
         for sample in range(metrics[study].num_samples):
             layout = metrics[study].layout[sample]
-            count_matrix: npt.NDArray = metrics[study].count_matrix.iloc[:, sample].values
-            gene_size = metrics[study].gene_sizes
-
-            count_matrix = count_matrix.astype(np.float32)
-            gene_size = gene_size.astype(np.float32)
+            count_matrix: npt.NDArray = metrics[study].count_matrix.iloc[:, sample].values.astype(np.float32)
+            gene_lengths = (
+                metrics[study].fragment_lengths[sample].astype(np.float32)
+                if layout == LayoutMethod.paired_end
+                else metrics[study].gene_sizes.astype(np.float32)
+            )
+            gene_lengths_kb = gene_lengths / 1000.0
 
             match layout:
                 case LayoutMethod.paired_end:  # FPKM
-                    mean_fragment_lengths = metrics[study].fragment_lengths[sample]
-                    # Ensure non-negative value
-                    effective_length = [max(1e-9, size - (mean_fragment_lengths + 1)) for size in gene_size]
-                    n = count_matrix.sum()
-                    fpkm = ((count_matrix + 1) * 1e9) / (np.array(effective_length) * n)
-                    matrix_values.append(fpkm)
+                    total_fragments = count_matrix.sum(axis=0)
+                    counts_per_million = total_fragments / scale
+                    fragments_per_kilobase = count_matrix / gene_lengths_kb[:, np.newaxis]
+                    fragments_per_kilobase_million = fragments_per_kilobase / counts_per_million
+                    matrix_values.append(fragments_per_kilobase_million)
                 case LayoutMethod.single_end:  # RPKM
-                    # Add a pseudocount before log to ensure log(0) does not happen
-                    rate = np.log(count_matrix + 1) - np.log(gene_size)
-                    exp_rate = np.exp(rate - np.log(np.sum(count_matrix)) + np.log(1e9))
-                    matrix_values.append(exp_rate)
+                    reads_per_kilobase = count_matrix / gene_lengths_kb[: np.newaxis]
+                    total_reads = count_matrix.sum(axis=0)
+                    counts_per_million = total_reads / scale
+                    reads_per_kilobase_million = reads_per_kilobase / counts_per_million
+                    matrix_values.append(reads_per_kilobase_million)
                 case _:
-                    raise ValueError("Invalid normalization method specified")
+                    _log_and_raise_error(
+                        (
+                            f"Invalid normalization method specified ''. "
+                            f"Must be one of '{LayoutMethod.paired_end.value}' or '{LayoutMethod.single_end.value}'."
+                        ),
+                        error=ValueError,
+                        level=LogLevel.ERROR,
+                    )
 
         fpkm_matrix = pd.DataFrame(matrix_values).T  # Transpose is needed because values were appended as rows
         fpkm_matrix = fpkm_matrix[~pd.isna(fpkm_matrix)]
         metrics[study].normalization_matrix = fpkm_matrix
-
         metrics[study].normalization_matrix.columns = metrics[study].count_matrix.columns
 
     return metrics

From 002c7053f3a9414b210b97541dcfa84439ca612d Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:43:58 -0600
Subject: [PATCH 163/345] style: ruff formatting

---
 main/como/rnaseq_gen.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index d0234ec3..97c81be8 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -278,7 +278,12 @@ def _calculate_fpkm(metrics: NamedMetrics, scale: int = 1e6) -> NamedMetrics:
     return metrics
 
 
-def _zfpkm_calculation(row: pd.Series, kernel: KernelDensity, peak_parameters: tuple[float, float]) -> _ZFPKMResult:
+# def _zfpkm_calculation(row: pd.Series, kernel: KernelDensity, peak_parameters: tuple[float, float]) -> _ZFPKMResult:
+def _zfpkm_calculation(
+    column: pd.Series,
+    peak_parameters: PeakIdentificationParameters,
+    bandwidth: int = 0.5,
+) -> _ZFPKMResult:
     """Log2 Transformations.
 
     Stabilize the variance in the data to make the distribution more symmetric; this is helpful for Gaussian fitting

From f8bf9983bfb008c1e9192c71f01104553912c1f0 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:44:07 -0600
Subject: [PATCH 164/345] style: fix docstring

---
 main/como/rnaseq_gen.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 97c81be8..4367001e 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -324,7 +324,7 @@ def _zfpkm_calculation(
         - Higher zFPKM values indicate higher expression levels relative to the "inactive" peak
         - A zFPKM value of 0 represents the mean of the "inactive" distribution
         - Research shows that a zFPKM value of -3 or greater can be used as
-            a threshold for calling a gene as "expressed"
+            a threshold for calling a gene as "active" and/or "expressed"
             : https://doi.org/10.1186/1471-2164-14-778
     """
     row_log2: npt.NDArray = np.log2(row + 1)

From e7f65e09c699e3abff3db65f12e6847237e9dbd0 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:44:57 -0600
Subject: [PATCH 165/345] fix: zfpkm calculations

Use column values instead of row values
---
 main/como/rnaseq_gen.py | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 4367001e..7935f51f 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -327,14 +327,12 @@ def _zfpkm_calculation(
             a threshold for calling a gene as "active" and/or "expressed"
             : https://doi.org/10.1186/1471-2164-14-778
     """
-    row_log2: npt.NDArray = np.log2(row + 1)
-    row_log2 = np.nan_to_num(row_log2, nan=0)
-    refit: KernelDensity = kernel.fit(row_log2.reshape(-1, 1))  # type: ignore
+    values = column.values
+    refit: KernelDensity = KernelDensity(kernel="gaussian", bandwidth=bandwidth).fit(values.reshape(-1, 1))  # type: ignore
 
-    # kde: KernelDensity = KernelDensity(kernel="gaussian", bandwidth=bandwidth).fit(row_log2.reshape(-1, 1))
-    x_range = np.linspace(row_log2.min(), row_log2.max(), 1000)
+    x_range = np.linspace(values.min(), values.max(), 2000)
     density = np.exp(refit.score_samples(x_range.reshape(-1, 1)))
-    peaks, _ = find_peaks(density, height=peak_parameters[0], distance=peak_parameters[1])
+    peaks, _ = find_peaks(density, height=peak_parameters.height, distance=peak_parameters.distance)
     peak_positions = x_range[peaks]
 
     mu = 0
@@ -344,9 +342,9 @@ def _zfpkm_calculation(
     if len(peaks) != 0:
         mu = peak_positions.max()
         max_fpkm = density[peaks[np.argmax(peak_positions)]]
-        u = row_log2[row_log2 > mu].mean()
+        u = values[values > mu].mean()
         stddev = (u - mu) * np.sqrt(np.pi / 2)
-    zfpkm = pd.Series((row_log2 - mu) / stddev, dtype=np.float32, name=row.name)
+    zfpkm = pd.Series((values - mu) / stddev, dtype=np.float32, name=column.name)
 
     return _ZFPKMResult(zfpkm=zfpkm, density=Density(x_range, density), mu=mu, std_dev=stddev, max_fpkm=max_fpkm)
 
@@ -365,13 +363,15 @@ def zfpkm_transform(
         )
         update_every_percent /= 100
 
-    total_rows = len(fpkm_df)
-    update_per_step: int = int(np.ceil(total_rows * update_every_percent))
-    cores = max(min(multiprocessing.cpu_count() - 2, total_rows), 1)  # Get at least 1 core and at most cpu_count() - 2
+    total_samples = _num_columns(fpkm_df)
+    update_per_step: int = int(np.ceil(total_samples * update_every_percent))
+
+    # Get at least 1 core and at most cpu_count() - 2
+    cores = max(min(multiprocessing.cpu_count() - 2, total_samples), 1)
     logger.debug(
-        f"zFPKM transforming {total_rows:,} gene(s) across {len(fpkm_df.columns)} sample(s) using {cores} cores"
+        f"zFPKM transforming {len(fpkm_df.columns)} sample(s) "
+        f"containing {len(fpkm_df):,} genes(s) using {cores} core(s)"
     )
-    logger.debug(f"Will update every {update_per_step:,} steps (~{update_every_percent:.1%} of {total_rows:,})")
 
     with Pool(processes=cores) as pool:
         kernel = KernelDensity(kernel="gaussian", bandwidth=bandwidth)

From 93e98442a929458e1af7f5d18bf6c906d7f91b3a Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:45:22 -0600
Subject: [PATCH 166/345] feat: use concurrent.futures for easier processing

---
 main/como/rnaseq_gen.py | 47 ++++++++++++++++++++---------------------
 1 file changed, 23 insertions(+), 24 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 7935f51f..1d541c69 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -372,44 +372,43 @@ def zfpkm_transform(
         f"zFPKM transforming {len(fpkm_df.columns)} sample(s) "
         f"containing {len(fpkm_df):,} genes(s) using {cores} core(s)"
     )
-
-    with Pool(processes=cores) as pool:
-        kernel = KernelDensity(kernel="gaussian", bandwidth=bandwidth)
-        chunksize = int(math.ceil(len(fpkm_df.columns) / (4 * cores)))
-        partial_func = partial(_zfpkm_calculation, kernel=kernel, peak_parameters=peak_parameters)
-        chunk_time = time.time()
-        start_time = time.time()
-        log_padding = len(str(f"{total_rows:,}"))
-
-        zfpkm_series: list[pd.Series | None] = [None] * total_rows
-        results: dict[str, _ZFPKMResult] = {}
-        result: _ZFPKMResult
-        for i, result in enumerate(
-            pool.imap(
-                partial_func,
-                (row for _, row in fpkm_df.iterrows()),
-                chunksize=chunksize,
+    logger.debug(f"Will update every {update_per_step:,} steps (~{update_every_percent:.1%} of {total_samples:,})")
+
+    chunk_time = time.time()
+    start_time = time.time()
+    log_padding = len(str(f"{total_samples:,}"))
+    zfpkm_series: list[pd.Series | None] = [None] * total_samples
+    results: dict[str, _ZFPKMResult] = {}
+
+    with ProcessPoolExecutor(max_workers=cores) as pool:
+        futures: list[Future[_ZFPKMResult]] = [
+            pool.submit(
+                _zfpkm_calculation,
+                column=fpkm_df[column],
+                peak_parameters=peak_parameters,
+                bandwidth=bandwidth,
             )
-        ):
+            for column in fpkm_df
+        ]
+        for i, future in enumerate(as_completed(futures)):
+            result = future.result()
             key = str(result.zfpkm.name)
             results[key] = result
             zfpkm_series[i] = result.zfpkm
 
-            # show updates every X% and at the end, but skip on first iteration
-            if i != 0 and (i % update_per_step == 0 or i >= total_rows):
+            if i != 0 and ((i + 1) % update_per_step == 0 or (i + 1) == total_samples):
                 current_time = time.time()
                 chunk = current_time - chunk_time
                 total_time = current_time - start_time
-                chunk_num = f"{i:,}"
+                chunk_num = f"{i + 1:,}"
                 logger.debug(
-                    f"Processed {chunk_num:>{log_padding}} of {total_rows:,} - "
+                    f"Processed {chunk_num:>{log_padding}} of {total_samples:,} - "
                     f"chunk took {chunk:.1f} seconds - "
                     f"running for {total_time:.1f} seconds"
                 )
                 chunk_time = current_time
 
-    zfpkm_df = pd.concat(zfpkm_series, axis=1)
-
+    zfpkm_df = pd.DataFrame({series.name: series for series in zfpkm_series}, index=fpkm_df.index)
     return results, zfpkm_df
 
 

From d6cb67705310d850562d1b1b482f5f9f4cc3051d Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:45:34 -0600
Subject: [PATCH 167/345] feat: allow setting bandwidth and peak parameters

---
 main/como/rnaseq_gen.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 1d541c69..913462c6 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -351,8 +351,8 @@ def _zfpkm_calculation(
 
 def zfpkm_transform(
     fpkm_df: pd.DataFrame,
-    bandwidth: int = 0.5,
-    peak_parameters: tuple[float, float] = (0.02, 1.0),
+    peak_parameters: PeakIdentificationParameters,
+    bandwidth: int,
     update_every_percent: float = 0.1,
 ) -> tuple[dict[str, _ZFPKMResult], DataFrame]:
     """Perform zFPKM calculation/transformation."""

From b2e5fe62231fbfd4286c3d33359f390c4fd41f73 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:45:46 -0600
Subject: [PATCH 168/345] style: ruff whitespace fixes

---
 main/como/rnaseq_gen.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 913462c6..72917d9d 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -443,8 +443,8 @@ def zfpkm_plot(results, *, plot_xfloor: int = -4, subplot_titles: bool = True):
         )
     mega_df = pd.concat(to_concat, ignore_index=True)
     mega_df.columns = pd.Series(data=["sample_name", "log2fpkm", "fpkm_density", "fitted_density_scaled"])
-
     mega_df = mega_df.melt(id_vars=["log2fpkm", "sample_name"], var_name="source", value_name="density")
+
     subplot_titles = list(results.keys()) if subplot_titles else None
     fig = make_subplots(
         rows=len(results),

From 720543f7522421a6f62c4797be85c02c0cf0672f Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:46:13 -0600
Subject: [PATCH 169/345] refactor: more efficient zfpkm plotting

---
 main/como/rnaseq_gen.py | 33 ++++++++++++++++++++++++++-------
 1 file changed, 26 insertions(+), 7 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 72917d9d..63e4fc04 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -453,16 +453,35 @@ def zfpkm_plot(results, *, plot_xfloor: int = -4, subplot_titles: bool = True):
         vertical_spacing=min(0.05, (1 / (len(results) - 1))),
     )
 
-    for i, (name, group) in enumerate(mega_df.groupby("sample_name"), start=1):
-        fig.add_trace(
-            trace=go.Scatter(x=group["log2fpkm"], y=group["density"], mode="lines", name=name, legendgroup=name),
-            row=i,
-            col=1,
-        )
-        fig.update_xaxes(title_text="log2(FPKM)", range=[plot_xfloor, max(group["log2fpkm"].tolist())], row=i, col=1)
+    for i, sample_name in enumerate(results, start=1):
+        sample_data = mega_df[mega_df["sample_name"] == sample_name]
+        traces = []
+        for source_type in sample_data["source"].unique():
+            group = sample_data[sample_data["source"] == source_type]
+            traces.append(
+                go.Scatter(
+                    x=group["log2fpkm"],
+                    y=group["density"],
+                    mode="lines",
+                    name=source_type,
+                    legendgroup=source_type,
+                )
+            )
+        fig.add_traces(traces, rows=i, cols=1)
+        fig.update_xaxes(title_text="log2(FPKM)", range=[plot_xfloor, sample_data["log2fpkm"].max()], row=i, col=1)
         fig.update_yaxes(title_text="density [scaled]", row=i, col=1)
         fig.update_layout(legend_tracegroupgap=0)
 
+    # for i, (name, group) in enumerate(mega_df.groupby("sample_name"), start=1):
+    #     fig.add_trace(
+    #         trace=go.Scatter(x=group["log2fpkm"], y=group["density"], mode="lines", name=name, legendgroup=name),
+    #         row=i,
+    #         col=1,
+    #     )
+    #     fig.update_xaxes(title_text="log2(FPKM)", range=[plot_xfloor, max(group["log2fpkm"].tolist())], row=i, col=1)
+    #     fig.update_yaxes(title_text="density [scaled]", row=i, col=1)
+    #     fig.update_layout(legend_tracegroupgap=0)
+
     fig.update_layout(height=600 * len(results), width=1000, title_text="zFPKM Plots", showlegend=True)
     fig.write_image("zfpkm_plot.png")
 

From 1daa66e5e8c319f66e27c66b1aea2cb12f2a3a7a Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:47:18 -0600
Subject: [PATCH 170/345] feat: use new RNAType Enum

---
 main/como/rnaseq_gen.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 63e4fc04..427237d2 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -502,7 +502,7 @@ def cpm_filter(
     context_name: str,
     metrics: NamedMetrics,
     filtering_options: _FilteringOptions,
-    prep: RNAPrepMethod,
+    prep: RNAType,
 ) -> NamedMetrics:
     """Apply Counts Per Million (CPM) filtering to the count matrix for a given sample."""
     config = Config()
@@ -632,7 +632,7 @@ def filter_counts(
     metrics: NamedMetrics,
     technique: FilteringTechnique,
     filtering_options: _FilteringOptions,
-    prep: RNAPrepMethod,
+    prep: RNAType,
 ) -> NamedMetrics:
     """Filter the count matrix based on the specified technique."""
     match technique:
@@ -655,7 +655,7 @@ async def _save_rnaseq_tests(
     rnaseq_matrix: pd.DataFrame,
     metadata_df: pd.DataFrame,
     gene_info_df: pd.DataFrame,
-    prep: RNAPrepMethod,
+    prep: RNAType,
     taxon: int,
     replicate_ratio: float,
     batch_ratio: float,
@@ -753,7 +753,7 @@ async def rnaseq_gen(
     context_name: str,
     input_rnaseq_filepath: Path,
     input_gene_info_filepath: Path,
-    prep: RNAPrepMethod,
+    prep: RNAType,
     taxon_id: int,
     output_boolean_activity_filepath: Path,
     output_zscore_normalization_filepath: Path,

From 0b005b768dd8e328848ed4df482b304b730341b6 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:47:29 -0600
Subject: [PATCH 171/345] fix: drop na values

---
 main/como/rnaseq_gen.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 427237d2..d9d8234c 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -527,6 +527,7 @@ def cpm_filter(
         counts_per_million: pd.DataFrame = (counts / library_size) * 1_000_000
         counts_per_million.insert(0, "entrez_gene_ids", pd.Series(entrez_ids))
         logger.debug(f"Writing CPM matrix to {output_filepath}")
+        counts_per_million.dropna(inplace=True)
         counts_per_million.to_csv(output_filepath, index=False)
 
         # TODO: Counts per million is adding ~61,500 columns (equal to the number of genes) for some reason.

From cb6e995e7f44551e7a3e5f911bacb66937ea708c Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:47:56 -0600
Subject: [PATCH 172/345] refactor: provide bandwidth and peak parameters

---
 main/como/rnaseq_gen.py | 23 +++++++++++++++++++----
 1 file changed, 19 insertions(+), 4 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index d9d8234c..8859a9d7 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -594,7 +594,15 @@ def tpm_quantile_filter(*, metrics: NamedMetrics, filtering_options: _FilteringO
     return metrics
 
 
-def zfpkm_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions, calculate_fpkm: bool) -> NamedMetrics:
+def zfpkm_filter(
+    *,
+    metrics: NamedMetrics,
+    filtering_options: _FilteringOptions,
+    calculate_fpkm: bool,
+    force_zfpkm_plot: bool,
+    peak_parameters: PeakIdentificationParameters,
+    bandwidth: int,
+) -> NamedMetrics:
     """Apply zFPKM filtering to the FPKM matrix for a given sample."""
     min_sample_expression = filtering_options.replicate_ratio
     high_confidence_sample_expression = filtering_options.high_replicate_ratio
@@ -605,12 +613,19 @@ def zfpkm_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions,
     for metric in metrics.values():
         # if fpkm was not calculated, the normalization matrix will be empty; collect the count matrix instead
         matrix = metric.count_matrix if metric.normalization_matrix.empty else metric.normalization_matrix
-        matrix = matrix[matrix.sum(axis=1) > 0]  # remove rows (genes) that have no counts
+        matrix = matrix[matrix.sum(axis=1) > 0]  # remove rows (genes) that have no counts across all samples
 
         minimums = matrix == 0
-        results, zfpkm_df = zfpkm_transform(matrix)
+        results, zfpkm_df = zfpkm_transform(matrix, peak_parameters=peak_parameters, bandwidth=bandwidth)
         zfpkm_df[minimums] = -4
-        zfpkm_plot(results)
+
+        if len(results) > 10 and not force_zfpkm_plot:
+            logger.warning(
+                "Not plotting zFPKM results because more than 10 plots would be created. "
+                "If you would like to plot them anyway, set 'force_zfpkm_plot' to True"
+            )
+        else:
+            zfpkm_plot(results)
 
         # determine which genes are expressed
         min_samples = round(min_sample_expression * len(zfpkm_df.columns))

From 8c16d943ba65876eeabc5254a774da789303b2ef Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:49:20 -0600
Subject: [PATCH 173/345] feat: allow force-plotting zfpkm

If more than ~10 samples exist, zFPKM plotting will not be done for efficiency sake. `force_zfpkm_plot` can be provided to force the plots to be made anyway
---
 main/como/rnaseq_gen.py | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 8859a9d7..43720216 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -649,6 +649,9 @@ def filter_counts(
     technique: FilteringTechnique,
     filtering_options: _FilteringOptions,
     prep: RNAType,
+    force_zfpkm_plot: bool,
+    peak_parameters: PeakIdentificationParameters,
+    bandwidth: int,
 ) -> NamedMetrics:
     """Filter the count matrix based on the specified technique."""
     match technique:
@@ -679,8 +682,11 @@ async def _save_rnaseq_tests(
     high_batch_ratio: float,
     technique: FilteringTechnique,
     cut_off: int | float,
+    force_zfpkm_plot: bool,
     output_boolean_activity_filepath: Path,
     output_zscore_normalization_filepath: Path,
+    peak_parameters: PeakIdentificationParameters,
+    bandwidth: int,
 ):
     """Save the results of the RNA-Seq tests to a CSV file."""
     filtering_options = _FilteringOptions(
@@ -706,6 +712,9 @@ async def _save_rnaseq_tests(
         technique=technique,
         filtering_options=filtering_options,
         prep=prep,
+        force_zfpkm_plot=force_zfpkm_plot,
+        peak_parameters=peak_parameters,
+        bandwidth=bandwidth,
     )
 
     merged_zscore_df = pd.DataFrame()
@@ -779,7 +788,11 @@ async def rnaseq_gen(
     batch_ratio: float = 0.5,
     high_batch_ratio: float = 1.0,
     technique: FilteringTechnique | str = FilteringTechnique.tpm,
+    zfpkm_peak_height: float = 0.02,
+    zfpkm_peak_distance: float = 1.0,
+    zfpkm_bandwidth: int = 1,
     cutoff: int | float | None = None,
+    force_zfpkm_plot: bool = False,
 ) -> None:
     """Generate a list of active and high-confidence genes from a gene count matrix.
 
@@ -803,7 +816,13 @@ async def rnaseq_gen(
     :param high_batch_ratio: The percentage of batches that a gene must
         appear in for a gene to be marked "highly confident" in its expression
     :param technique: The filtering technique to use
+    :param zfpkm_peak_height: The height of the zFPKM peak
+    :param zfpkm_peak_distance: The distance of the zFPKM peak
+    :param zfpkm_bandwidth: The bandwidth of the zFPKM
     :param cutoff: The cutoff value to use for the provided filtering technique
+    :param force_zfpkm_plot: If too many samples exist, should plotting be done anyway?
+    :param log_level: The level of logging to output
+    :param log_location: The location to write logs to
     :return: None
     """
     technique = (

From 97c7d1db7fd5a32702e6730ed87547592a22e54c Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:50:23 -0600
Subject: [PATCH 174/345] refactor: rename FilteringTechnique constants

---
 main/como/rnaseq_gen.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 43720216..4930a2ae 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -655,11 +655,11 @@ def filter_counts(
 ) -> NamedMetrics:
     """Filter the count matrix based on the specified technique."""
     match technique:
-        case FilteringTechnique.cpm:
+        case FilteringTechnique.CPM:
             return cpm_filter(
                 context_name=context_name, metrics=metrics, filtering_options=filtering_options, prep=prep
             )
-        case FilteringTechnique.tpm:
+        case FilteringTechnique.TPM:
             return tpm_quantile_filter(metrics=metrics, filtering_options=filtering_options)
         case FilteringTechnique.zfpkm:
             return zfpkm_filter(metrics=metrics, filtering_options=filtering_options, calculate_fpkm=True)
@@ -787,7 +787,7 @@ async def rnaseq_gen(
     high_replicate_ratio: float = 1.0,
     batch_ratio: float = 0.5,
     high_batch_ratio: float = 1.0,
-    technique: FilteringTechnique | str = FilteringTechnique.tpm,
+    technique: FilteringTechnique | str = FilteringTechnique.ZFPKM,
     zfpkm_peak_height: float = 0.02,
     zfpkm_peak_distance: float = 1.0,
     zfpkm_bandwidth: int = 1,
@@ -830,18 +830,18 @@ async def rnaseq_gen(
     )
 
     match technique:
-        case FilteringTechnique.tpm:
+        case FilteringTechnique.TPM:
             cutoff = cutoff or 25
             if cutoff < 1 or cutoff > 100:
                 raise ValueError("Quantile must be between 1 - 100")
 
-        case FilteringTechnique.cpm:
+        case FilteringTechnique.CPM:
             if cutoff and cutoff < 0:
                 raise ValueError("Cutoff must be greater than 0")
             elif cutoff:
                 cutoff = "default"
 
-        case FilteringTechnique.zfpkm:
+        case FilteringTechnique.ZFPKM | FilteringTechnique.UMI:
             cutoff = cutoff or -3
         case FilteringTechnique.umi:
             pass
@@ -851,8 +851,8 @@ async def rnaseq_gen(
     if not input_rnaseq_filepath.exists():
         raise FileNotFoundError(f"Input RNA-seq file not found! Searching for: '{input_rnaseq_filepath}'")
 
-    if prep == RNAPrepMethod.SCRNA:
         technique = FilteringTechnique.umi
+    if prep == RNAType.SCRNA and technique.value.lower() != FilteringTechnique.UMI.value.lower():
         logger.warning(
             "Single cell filtration does not normalize and assumes "
             "gene counts are counted with Unique Molecular Identifiers (UMIs). "

From b67cd842c7e03d7cb5c77ea8d210e48a6a15c317 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:51:33 -0600
Subject: [PATCH 175/345] style: ruff whitespace and log message formatting

---
 main/como/rnaseq_gen.py | 39 +++++++++++++++++++++++++++++----------
 1 file changed, 29 insertions(+), 10 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 4930a2ae..6a1b0bed 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -661,12 +661,31 @@ def filter_counts(
             )
         case FilteringTechnique.TPM:
             return tpm_quantile_filter(metrics=metrics, filtering_options=filtering_options)
-        case FilteringTechnique.zfpkm:
-            return zfpkm_filter(metrics=metrics, filtering_options=filtering_options, calculate_fpkm=True)
-        case FilteringTechnique.umi:
-            return zfpkm_filter(metrics=metrics, filtering_options=filtering_options, calculate_fpkm=False)
+        case FilteringTechnique.ZFPKM:
+            return zfpkm_filter(
+                metrics=metrics,
+                filtering_options=filtering_options,
+                calculate_fpkm=True,
+                force_zfpkm_plot=force_zfpkm_plot,
+                peak_parameters=peak_parameters,
+                bandwidth=bandwidth,
+            )
+        case FilteringTechnique.UMI:
+            # UMI filtering is the same as zFPKM filtering without calculating FPKM
+            return zfpkm_filter(
+                metrics=metrics,
+                filtering_options=filtering_options,
+                calculate_fpkm=False,
+                force_zfpkm_plot=force_zfpkm_plot,
+                peak_parameters=peak_parameters,
+                bandwidth=bandwidth,
+            )
         case _:
-            raise ValueError(f"Technique must be one of {FilteringTechnique}")
+            _log_and_raise_error(
+                f"Technique must be one of {FilteringTechnique}, got '{technique.value}'",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
 
 async def _save_rnaseq_tests(
@@ -760,7 +779,8 @@ async def _save_rnaseq_tests(
 
     boolean_matrix.to_csv(output_boolean_activity_filepath, index=False)
     logger.info(
-        f"{context_name} - Found {expressed_count} expressed and {high_confidence_count} confidently expressed genes"
+        f"{context_name} - Found {expressed_count} expressed genes, "
+        f"{high_confidence_count} of which are confidently expressed"
     )
     logger.success(f"Wrote boolean matrix to {output_boolean_activity_filepath}")
 
@@ -843,8 +863,6 @@ async def rnaseq_gen(
 
         case FilteringTechnique.ZFPKM | FilteringTechnique.UMI:
             cutoff = cutoff or -3
-        case FilteringTechnique.umi:
-            pass
         case _:
             raise ValueError(f"Technique must be one of {FilteringTechnique}")
 
@@ -855,8 +873,9 @@ async def rnaseq_gen(
     if prep == RNAType.SCRNA and technique.value.lower() != FilteringTechnique.UMI.value.lower():
         logger.warning(
             "Single cell filtration does not normalize and assumes "
-            "gene counts are counted with Unique Molecular Identifiers (UMIs). "
-            "Setting filtering technique to UMI now."
+            "genes are counted with Unique Molecular Identifiers (UMIs). "
+            f"Switching filtering technique from '{technique.value}' to '{FilteringTechnique.UMI.value}'."
+        )
         )
 
     logger.debug(f"Starting '{context_name}'")

From 638de62ff1d84fa6debbe43dce166c3fe71b80f8 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:52:20 -0600
Subject: [PATCH 176/345] refactor: use _process, not _save_rnaseq_tests

Makes more sense as to what is actually happening when calling this internal function
---
 main/como/rnaseq_gen.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 6a1b0bed..d61d970d 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -688,7 +688,7 @@ def filter_counts(
             )
 
 
-async def _save_rnaseq_tests(
+async def _process(
     context_name: str,
     rnaseq_matrix: pd.DataFrame,
     metadata_df: pd.DataFrame,
@@ -885,7 +885,7 @@ async def rnaseq_gen(
         if isinstance(input_metadata_filepath_or_df, pd.DataFrame)
         else await _create_metadata_df(input_metadata_filepath_or_df)
     )
-    await _save_rnaseq_tests(
+    await _process(
         context_name=context_name,
         rnaseq_matrix=await _read_counts(input_rnaseq_filepath),
         metadata_df=metadata_df,

From ba852371c2e07bef44881742d72335ca248ba2b6 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:52:43 -0600
Subject: [PATCH 177/345] refactor: provide path to rnaseq matrix

---
 main/como/rnaseq_gen.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index d61d970d..c4050a4d 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -690,7 +690,7 @@ def filter_counts(
 
 async def _process(
     context_name: str,
-    rnaseq_matrix: pd.DataFrame,
+    rnaseq_matrix_filepath: Path,
     metadata_df: pd.DataFrame,
     gene_info_df: pd.DataFrame,
     prep: RNAType,
@@ -887,7 +887,7 @@ async def rnaseq_gen(
     )
     await _process(
         context_name=context_name,
-        rnaseq_matrix=await _read_counts(input_rnaseq_filepath),
+        rnaseq_matrix_filepath=input_rnaseq_filepath,
         metadata_df=metadata_df,
         gene_info_df=pd.read_csv(input_gene_info_filepath),
         prep=prep,

From 681e871f17e2238df3e4b3acaba7571f4f3024c2 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:52:56 -0600
Subject: [PATCH 178/345] refactor: process provided rnaseq matrix

---
 main/como/rnaseq_gen.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index c4050a4d..985292a3 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -708,6 +708,17 @@ async def _process(
     bandwidth: int,
 ):
     """Save the results of the RNA-Seq tests to a CSV file."""
+    output_boolean_activity_filepath.parent.mkdir(parents=True, exist_ok=True)
+
+    rnaseq_matrix: pd.DataFrame = await _read_file(rnaseq_matrix_filepath)
+    if rnaseq_matrix_filepath.suffix == ".h5ad":
+        conversion = await gene_symbol_to_ensembl_and_gene_id(
+            symbols=rnaseq_matrix["gene_symbol"].tolist(), taxon=taxon
+        )
+        conversion.reset_index(inplace=True)
+        rnaseq_matrix = rnaseq_matrix.merge(conversion, how="left", on="gene_symbol")
+        rnaseq_matrix.replace(to_replace=pd.NA, value="-")
+
     filtering_options = _FilteringOptions(
         replicate_ratio=replicate_ratio,
         batch_ratio=batch_ratio,

From f4d6be9d19a1e2f591ecf40f961ff312390a534d Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:53:32 -0600
Subject: [PATCH 179/345] fix: proper check if dataframe is empty/None

---
 main/como/rnaseq_gen.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 985292a3..a72e7f82 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -753,12 +753,12 @@ async def _process(
     for metric in metrics.values():
         expressed_genes.extend(metric.entrez_gene_ids)
         top_genes.extend(metric.high_confidence_entrez_gene_ids)
-        if metric.normalization_matrix is not None:
+        if not metric.normalization_matrix.empty:
             merged_zscore_df = (
-                metric.normalization_matrix
+                metric.z_score_matrix
                 if merged_zscore_df.empty
                 else pd.concat(
-                    [merged_zscore_df, metric.normalization_matrix],
+                    [merged_zscore_df, metric.z_score_matrix],
                     axis=1,
                 )
             )

From 54cd8ac27f9cb662296c72bd1df07a7f9637edb2 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:53:49 -0600
Subject: [PATCH 180/345] fix: only write normalized matrix if data exists

---
 main/como/rnaseq_gen.py | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index a72e7f82..6e0f334b 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -762,9 +762,18 @@ async def _process(
                     axis=1,
                 )
             )
-    merged_zscore_df.index = pd.Series(entrez_gene_ids, name="entrez_gene_id")
-    merged_zscore_df.to_csv(output_zscore_normalization_filepath, index=True)
-    logger.success(f"Wrote z-score normalization matrix to {output_zscore_normalization_filepath}")
+
+    # If any of the normalization metrics are not empty, write the normalized metrics to disk
+    if not all(metric.normalization_matrix.empty for metric in metrics.values()):
+        merged_zscore_df.index = pd.Series(entrez_gene_ids, name="entrez_gene_id")
+        merged_zscore_df.dropna(inplace=True)
+        merged_zscore_df.to_csv(output_zscore_normalization_filepath, index=True)
+        logger.success(f"Wrote z-score normalization matrix to {output_zscore_normalization_filepath}")
+    else:
+        logger.warning(
+            "Not writing z-score normalization matrix because no normalization matrices exist. "
+            "This is expected if you are using UMI filtering."
+        )
 
     expression_frequency = pd.Series(expressed_genes).value_counts()
     expression_df = pd.DataFrame(

From ea9654d8c7be772d508d4835652e946e1d373468 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:54:04 -0600
Subject: [PATCH 181/345] fix: proper calculation of high confidence genes

---
 main/como/rnaseq_gen.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 6e0f334b..8d452c0b 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -795,8 +795,9 @@ async def _process(
             boolean_matrix.loc[gene, "high"] = 1
 
     expressed_count = len(boolean_matrix[boolean_matrix["expressed"] == 1])
-    high_confidence_count = len(boolean_matrix[boolean_matrix["high"] == 1])
+    high_confidence_count = len(boolean_matrix[boolean_matrix["high"] == 1]) - expressed_count
 
+    boolean_matrix.dropna(subset="entrez_gene_id", inplace=True)
     boolean_matrix.to_csv(output_boolean_activity_filepath, index=False)
     logger.info(
         f"{context_name} - Found {expressed_count} expressed genes, "

From e663406a7fac8f8f5e2e2280b716d983351b52e7 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:54:15 -0600
Subject: [PATCH 182/345] refactor: remove internal _create_metadata_df func

---
 main/como/rnaseq_gen.py | 9 ---------
 1 file changed, 9 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 8d452c0b..265b6383 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -806,15 +806,6 @@ async def _process(
     logger.success(f"Wrote boolean matrix to {output_boolean_activity_filepath}")
 
 
-async def _create_metadata_df(path: Path) -> pd.DataFrame:
-    if path.suffix not in {".xls", ".xlsx"}:
-        raise ValueError(
-            f"Expected an excel file with extension of '.xlsx' or '.xls', got '{path.suffix}'. "
-            f"Attempted to process: {path}"
-        )
-    return pd.read_excel(path)
-
-
 async def rnaseq_gen(
     context_name: str,
     input_rnaseq_filepath: Path,

From fda629ec5a455e5786ed9b405c41a23c2c897d8f Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:54:36 -0600
Subject: [PATCH 183/345] refactor: proper usage of _set_up_logging function

---
 main/como/rnaseq_gen.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 265b6383..507e3463 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -825,6 +825,8 @@ async def rnaseq_gen(
     zfpkm_bandwidth: int = 1,
     cutoff: int | float | None = None,
     force_zfpkm_plot: bool = False,
+    log_level: LogLevel = LogLevel.INFO,
+    log_location: str | TextIOWrapper = sys.stderr,
 ) -> None:
     """Generate a list of active and high-confidence genes from a gene count matrix.
 
@@ -857,10 +859,9 @@ async def rnaseq_gen(
     :param log_location: The location to write logs to
     :return: None
     """
-    technique = (
-        FilteringTechnique.from_string(str(technique.lower())) if isinstance(technique, (str, int)) else technique
-    )
+    _set_up_logging(level=log_level, location=log_location)
 
+    technique = FilteringTechnique(technique) if isinstance(technique, str) else technique
     match technique:
         case FilteringTechnique.TPM:
             cutoff = cutoff or 25

From 59f7f955c02d84fdd3c47cefa37057bd513769d1 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:55:03 -0600
Subject: [PATCH 184/345] fix: proper usage of _log_and_raise_error function

---
 main/como/rnaseq_gen.py | 25 ++++++++++++++++++++-----
 1 file changed, 20 insertions(+), 5 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 507e3463..adab17b0 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -866,23 +866,38 @@ async def rnaseq_gen(
         case FilteringTechnique.TPM:
             cutoff = cutoff or 25
             if cutoff < 1 or cutoff > 100:
-                raise ValueError("Quantile must be between 1 - 100")
+                _log_and_raise_error(
+                    "Quantile must be between 1 - 100",
+                    error=ValueError,
+                    level=LogLevel.ERROR,
+                )
 
         case FilteringTechnique.CPM:
             if cutoff and cutoff < 0:
-                raise ValueError("Cutoff must be greater than 0")
+                _log_and_raise_error(
+                    "Cutoff must be greater than or equal to 0",
+                    error=ValueError,
+                    level=LogLevel.ERROR,
+                )
             elif cutoff:
                 cutoff = "default"
 
         case FilteringTechnique.ZFPKM | FilteringTechnique.UMI:
             cutoff = cutoff or -3
         case _:
-            raise ValueError(f"Technique must be one of {FilteringTechnique}")
+            _log_and_raise_error(
+                f"Technique must be one of {','.join(FilteringTechnique)}. Got: {technique.value}",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
     if not input_rnaseq_filepath.exists():
-        raise FileNotFoundError(f"Input RNA-seq file not found! Searching for: '{input_rnaseq_filepath}'")
+        _log_and_raise_error(
+            f"Input RNA-seq file not found! Searching for: '{input_rnaseq_filepath}'",
+            error=FileNotFoundError,
+            level=LogLevel.ERROR,
+        )
 
-        technique = FilteringTechnique.umi
     if prep == RNAType.SCRNA and technique.value.lower() != FilteringTechnique.UMI.value.lower():
         logger.warning(
             "Single cell filtration does not normalize and assumes "

From 3b63b7d2bfd7e89e51e2fa70b1f9a190b34b7457 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:55:20 -0600
Subject: [PATCH 185/345] refactor: proper processing of metadata

---
 main/como/rnaseq_gen.py | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index adab17b0..58f209a4 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -904,6 +904,33 @@ async def rnaseq_gen(
             "genes are counted with Unique Molecular Identifiers (UMIs). "
             f"Switching filtering technique from '{technique.value}' to '{FilteringTechnique.UMI.value}'."
         )
+        technique = FilteringTechnique.UMI
+
+    if isinstance(input_metadata_filepath_or_df, pd.DataFrame):
+        metadata_df = input_metadata_filepath_or_df
+    elif isinstance(input_metadata_filepath_or_df, Path):
+        if input_metadata_filepath_or_df.suffix not in {".xlsx", ".xls"}:
+            _log_and_raise_error(
+                (
+                    f"Expected an excel file with extension of '.xlsx' or '.xls', "
+                    f"got '{input_metadata_filepath_or_df.suffix}'."
+                ),
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
+        if not input_metadata_filepath_or_df.exists():
+            _log_and_raise_error(
+                f"Input metadata file not found! Searching for: '{input_metadata_filepath_or_df}'",
+                error=FileNotFoundError,
+                level=LogLevel.ERROR,
+            )
+
+        metadata_df = pd.read_excel(input_metadata_filepath_or_df)
+    else:
+        _log_and_raise_error(
+            f"Expected a pandas DataFrame or Path object as metadata, got '{type(input_metadata_filepath_or_df)}'",
+            error=TypeError,
+            level=LogLevel.ERROR,
         )
 
     logger.debug(f"Starting '{context_name}'")

From 0ff2eb7227e185b14932f3a016d4b1ce2dc2aa58 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:55:42 -0600
Subject: [PATCH 186/345] refactor: move logic to out of main function

---
 main/como/rnaseq_gen.py | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 58f209a4..3b602c36 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -934,12 +934,6 @@ async def rnaseq_gen(
         )
 
     logger.debug(f"Starting '{context_name}'")
-    output_boolean_activity_filepath.parent.mkdir(parents=True, exist_ok=True)
-    metadata_df = (
-        input_metadata_filepath_or_df
-        if isinstance(input_metadata_filepath_or_df, pd.DataFrame)
-        else await _create_metadata_df(input_metadata_filepath_or_df)
-    )
     await _process(
         context_name=context_name,
         rnaseq_matrix_filepath=input_rnaseq_filepath,

From 777064382cb22fb50b75fd652db6843fdc671561 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:55:54 -0600
Subject: [PATCH 187/345] fix: add proper function parameters

---
 main/como/rnaseq_gen.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 3b602c36..8fe2780a 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -938,7 +938,7 @@ async def rnaseq_gen(
         context_name=context_name,
         rnaseq_matrix_filepath=input_rnaseq_filepath,
         metadata_df=metadata_df,
-        gene_info_df=pd.read_csv(input_gene_info_filepath),
+        gene_info_df=await _read_file(input_gene_info_filepath),
         prep=prep,
         taxon=taxon_id,
         replicate_ratio=replicate_ratio,
@@ -947,6 +947,9 @@ async def rnaseq_gen(
         high_batch_ratio=high_batch_ratio,
         technique=technique,
         cut_off=cutoff,
+        force_zfpkm_plot=force_zfpkm_plot,
         output_boolean_activity_filepath=output_boolean_activity_filepath,
         output_zscore_normalization_filepath=output_zscore_normalization_filepath,
+        peak_parameters=PeakIdentificationParameters(height=zfpkm_peak_height, distance=zfpkm_peak_distance),
+        bandwidth=zfpkm_bandwidth,
     )

From bdc0f05d63674dc67209b04b1256d507102d93ef Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:58:08 -0600
Subject: [PATCH 188/345] refactor: import required modules

---
 main/como/proteomics_gen.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/main/como/proteomics_gen.py b/main/como/proteomics_gen.py
index d020d71d..ecf568bf 100644
--- a/main/como/proteomics_gen.py
+++ b/main/como/proteomics_gen.py
@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+import sys
+from io import TextIOWrapper
 from pathlib import Path
 
 import numpy as np
@@ -7,9 +9,10 @@
 from fast_bioservices.biodbnet import BioDBNet, Input, Output
 from loguru import logger
 
-from como import return_placeholder_data
+from como.data_types import LOG_FORMAT, LogLevel
 from como.project import Config
 from como.proteomics_preprocessing import protein_transform_main
+from como.utils import _log_and_raise_error, _set_up_logging, return_placeholder_data
 
 
 # Load Proteomics

From 475b27a2c1ecc69bbcd402cd82f221ec4f21f76c Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:58:21 -0600
Subject: [PATCH 189/345] fix: proper calling of _log_and_raise_error

---
 main/como/proteomics_gen.py | 40 ++++++++++++++++++++++++++++++-------
 1 file changed, 33 insertions(+), 7 deletions(-)

diff --git a/main/como/proteomics_gen.py b/main/como/proteomics_gen.py
index ecf568bf..b3ffe352 100644
--- a/main/como/proteomics_gen.py
+++ b/main/como/proteomics_gen.py
@@ -21,7 +21,11 @@ def process_proteomics_data(path: Path) -> pd.DataFrame:
     # Preprocess data, drop na, duplicate ';' in symbol,
     matrix: pd.DataFrame = pd.read_csv(path)
     if "gene_symbol" not in matrix.columns:
-        raise ValueError("No gene_symbol column found in proteomics data.")
+        _log_and_raise_error(
+            "No gene_symbol column found in proteomics data.",
+            error=ValueError,
+            level=LogLevel.ERROR,
+        )
 
     matrix["gene_symbol"] = matrix["gene_symbol"].astype(str)
     matrix.dropna(subset=["gene_symbol"], inplace=True)
@@ -130,7 +134,9 @@ def load_empty_dict():
 
     inquiry_full_path = config.data_dir / "config_sheets" / filename
     if not inquiry_full_path.exists():
-        raise FileNotFoundError(f"Error: file not found {inquiry_full_path}")
+        _log_and_raise_error(
+            f"Error: file not found {inquiry_full_path}", error=FileNotFoundError, level=LogLevel.ERROR
+        )
 
     filename = f"Proteomics_{context_name}.csv"
     full_save_filepath = config.result_dir / context_name / "proteomics" / filename
@@ -164,17 +170,37 @@ async def proteomics_gen(
 ):
     """Generate proteomics data."""
     if not config_filepath.exists():
-        raise FileNotFoundError(f"Config file not found at {config_filepath}")
+        _log_and_raise_error(
+            f"Config file not found at {config_filepath}",
+            error=FileNotFoundError,
+            level=LogLevel.ERROR,
+        )
     if config_filepath.suffix not in (".xlsx", ".xls"):
-        raise FileNotFoundError(f"Config file must be an xlsx or xls file at {config_filepath}")
+        _log_and_raise_error(
+            f"Config file must be an xlsx or xls file at {config_filepath}",
+            error=FileNotFoundError,
+            level=LogLevel.ERROR,
+        )
 
     if not matrix_filepath.exists():
-        raise FileNotFoundError(f"Matrix file not found at {matrix_filepath}")
+        _log_and_raise_error(
+            f"Matrix file not found at {matrix_filepath}",
+            error=FileNotFoundError,
+            level=LogLevel.ERROR,
+        )
     if matrix_filepath.suffix not in {".csv"}:
-        raise FileNotFoundError(f"Matrix file must be a csv file at {matrix_filepath}")
+        _log_and_raise_error(
+            f"Matrix file must be a csv file at {matrix_filepath}",
+            error=FileNotFoundError,
+            level=LogLevel.ERROR,
+        )
 
     if quantile < 0 or quantile > 100:
-        raise ValueError("Quantile must be an integer from 0 to 100")
+        _log_and_raise_error(
+            "Quantile must be an integer from 0 to 100",
+            error=ValueError,
+            level=LogLevel.ERROR,
+        )
     quantile /= 100
 
     config_df = pd.read_excel(config_filepath, sheet_name=context_name)

From 2c65b0874e56f16a4ffb37a452ce9636091cf1ee Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:58:46 -0600
Subject: [PATCH 190/345] feat: allow providing log level and location

---
 main/como/proteomics_gen.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/main/como/proteomics_gen.py b/main/como/proteomics_gen.py
index b3ffe352..dd50a75d 100644
--- a/main/como/proteomics_gen.py
+++ b/main/como/proteomics_gen.py
@@ -167,8 +167,12 @@ async def proteomics_gen(
     high_confidence_replicate_ratio: float = 0.7,
     high_confidence_batch_ratio: float = 0.7,
     quantile: int = 25,
+    log_level: LogLevel = LogLevel.INFO,
+    log_location: str | TextIOWrapper = sys.stderr,
 ):
     """Generate proteomics data."""
+    _set_up_logging(level=log_level, location=log_location)
+
     if not config_filepath.exists():
         _log_and_raise_error(
             f"Config file not found at {config_filepath}",

From e9d919373ebffe2ba85dee1372964b0af9ccff99 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:59:04 -0600
Subject: [PATCH 191/345] refactor: import required modules

---
 main/como/merge_xomics.py | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index f58a69b3..911c0d80 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -1,6 +1,8 @@
 from __future__ import annotations
 
-from enum import Enum
+import asyncio
+import sys
+from io import TextIOWrapper
 from pathlib import Path
 
 import numpy as np
@@ -8,17 +10,22 @@
 from fast_bioservices.biothings.mygene import MyGene
 from loguru import logger
 
-from como import return_placeholder_data
 from como.combine_distributions import (
+    _begin_combining_distributions,
+)
+from como.data_types import (
+    AdjustmentMethod,
+    LogLevel,
+    RNAType,
+    SourceTypes,
     _BatchEntry,
     _BatchNames,
-    _combine_zscores,
     _InputMatrices,
     _OutputCombinedSourceFilepath,
     _SourceWeights,
 )
-from como.data_types import RNAPrepMethod
 from como.project import Config
+from como.utils import _log_and_raise_error, _read_file, _set_up_logging, get_missing_gene_data, return_placeholder_data
 
 
 class _MergedHeaderNames:

From 632bfa8f3d9d37a3f0419e4160679cd09129a643 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 14:59:40 -0600
Subject: [PATCH 192/345] refactor: remove data-specific Enums

These Enums are now found in data_types.py
---
 main/como/merge_xomics.py | 15 ---------------
 1 file changed, 15 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 911c0d80..15c3d350 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -49,21 +49,6 @@ class _HighExpressionHeaderNames:
     PROTEOMICS = f"{_MergedHeaderNames.PROTEOMICS}_high"
 
 
-class AdjustmentMethod(Enum):
-    """Adjustment method for expression requirement based on differences in number of provided data source types."""
-
-    PROGRESSIVE = "progressive"
-    REGRESSIVE = "regressive"
-    FLAT = "flat"
-    CUSTOM = "custom"
-
-    @classmethod
-    def from_string(cls, value: str) -> AdjustmentMethod:
-        """Convert a string to an AdjustmentMethod enum."""
-        if value.lower() not in [t.value for t in cls]:
-            raise ValueError(f"Adjustment method must be one of {cls}; got: {value}")
-        return cls(value)
-
 
 def _load_rnaseq_tests(filename, context_name, prep_method: RNAPrepMethod) -> tuple[str, pd.DataFrame]:
     """Load rnaseq results."""

From f9669a64fd802713a35b64a1aabf741bbde6c444 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:00:03 -0600
Subject: [PATCH 193/345] refactor: async data loading

---
 main/como/merge_xomics.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 15c3d350..0bef6ac7 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -49,9 +49,10 @@ class _HighExpressionHeaderNames:
     PROTEOMICS = f"{_MergedHeaderNames.PROTEOMICS}_high"
 
 
-
-def _load_rnaseq_tests(filename, context_name, prep_method: RNAPrepMethod) -> tuple[str, pd.DataFrame]:
+# TODO: If function is no longer needed, remove?
+async def _load_rnaseq_tests(filename, context_name, prep_method: RNAType) -> tuple[str, pd.DataFrame]:
     """Load rnaseq results."""
+    logger.debug(f"Loading data for context '{context_name}' using preparation method '{prep_method.value}'")
     config = Config()
 
     def load_dummy_dict():
@@ -62,8 +63,12 @@ def load_dummy_dict():
         return load_dummy_dict()
 
     inquiry_full_path = config.data_dir / "config_sheets" / filename
-    if not inquiry_full_path.exists():  # check that config file exist (isn't needed to load, but helps user)
-        raise FileNotFoundError(f"Error: Config file not found at {inquiry_full_path}")
+    if not inquiry_full_path.exists():
+        _log_and_raise_error(
+            f"Config file not found at {inquiry_full_path}",
+            error=FileNotFoundError,
+            level=LogLevel.ERROR,
+        )
 
     match prep_method:
         case RNAPrepMethod.TOTAL:

From 938f6cb5fa7aebac4cdc777422affd43009e15fb Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:01:01 -0600
Subject: [PATCH 194/345] refactor: use new RNAType; _log_and_raise_error

---
 main/como/merge_xomics.py | 21 ++++++++++++---------
 1 file changed, 12 insertions(+), 9 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 0bef6ac7..b176696b 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -71,21 +71,24 @@ def load_dummy_dict():
         )
 
     match prep_method:
-        case RNAPrepMethod.TOTAL:
-            filename = f"rnaseq_total_{context_name}.csv"
-        case RNAPrepMethod.MRNA:
-            filename = f"rnaseq_mrna_{context_name}.csv"
-        case RNAPrepMethod.SCRNA:
-            filename = f"rnaseq_scrna_{context_name}.csv"
+        case RNAType.TRNA:
+            filename = f"{RNAType.TRNA.value}_{context_name}.csv"
+        case RNAType.MRNA:
+            filename = f"{RNAType.MRNA.value}_{context_name}.csv"
+        case RNAType.SCRNA:
+            filename = f"{RNAType.SCRNA.value}_{context_name}.csv"
         case _:
-            raise ValueError(
-                f"Unsupported RNA-seq library type: {prep_method.value}. "
-                f"Must be an option defined in 'RNASeqPreparationMethod'."
+            _log_and_raise_error(
+                f"Unsupported RNA-seq library type: {prep_method.value}. Must be one of {', '.join(RNAType)}.",
+                error=ValueError,
+                level=LogLevel.ERROR,
             )
 
     save_filepath = config.result_dir / context_name / prep_method.value / filename
     if save_filepath.exists():
+        logger.debug(f"Loading RNA-seq data from: {save_filepath}")
         data = pd.read_csv(save_filepath, index_col="entrez_gene_id")
+        logger.success(f"Successfully loaded RNA-seq data from: {save_filepath}")
         return context_name, data
 
     else:

From ed2044ed3d10e1dd9878c0bd4dcf7b3accb742fe Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:01:12 -0600
Subject: [PATCH 195/345] refactor: drop na values

---
 main/como/merge_xomics.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index b176696b..57d7f7ce 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -107,8 +107,7 @@ def _merge_logical_table(df: pd.DataFrame):
     :return: pandas dataframe of merged table
     """
     # step 1: get all plural ENTREZ_GENE_IDs in the input table, extract unique IDs
-
-    df.dropna(axis=0, subset=["entrez_gene_id"], inplace=True)
+    df.dropna(subset=["entrez_gene_id"], inplace=True)
     df["entrez_gene_id"] = df["entrez_gene_id"].astype(str).str.replace(" /// ", "//").astype(str)
 
     id_list: list[str] = df.loc[

From 6074d6b25b6626a7e2aa5397d53f053ba8353e5e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:02:02 -0600
Subject: [PATCH 196/345] feat: add logging

---
 main/como/merge_xomics.py | 34 ++++++++++++++++++++++++++++++++--
 1 file changed, 32 insertions(+), 2 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 57d7f7ce..75b05be2 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -120,6 +120,7 @@ def _merge_logical_table(df: pd.DataFrame):
     for i in multiple_entrez_ids:
         ids = i.split("//")
         id_list.extend(ids)
+        logger.trace(f"Processing multiple IDs {ids} for {i}")
 
         duplicate_rows = pd.DataFrame([])
         for j in ids:
@@ -129,15 +130,19 @@ def _merge_logical_table(df: pd.DataFrame):
 
         df = pd.concat([df, pd.DataFrame(duplicate_rows)], axis=0, ignore_index=True)
         df.drop(df[df["entrez_gene_id"] == i].index, inplace=True)
+    logger.trace(f"Shape after merging duplciated rows: {df.shape}")
 
     full_entrez_id_sets: set[str] = set()
     entrez_dups_list: list[list[str]] = []
     multi_entrez_index = list(range(len(multiple_entrez_ids)))
 
+    logger.trace("Starting to merge multiple entrez IDs")
     for i in range(len(multiple_entrez_ids)):
         if i not in multi_entrez_index:
             continue
 
+        logger.trace(f"Iterating through multi-entrez ids, index {i}")
+
         set1 = set(multiple_entrez_ids[i].split("//"))
         multi_entrez_index.remove(i)
 
@@ -153,14 +158,17 @@ def _merge_logical_table(df: pd.DataFrame):
         new_entrez_id = " /// ".join(sortlist)
         full_entrez_id_sets.add(new_entrez_id)
 
+    logger.debug(f"Finished merging multiple entrez IDs, found {len(full_entrez_id_sets)} sets")
     entrez_dups_list.extend(i.split(" /// ") for i in full_entrez_id_sets)
     entrez_dups_dict = dict(zip(full_entrez_id_sets, entrez_dups_list))
 
+    logger.trace("Replacing IDs in dataframe")
     for merged_entrez_id, entrez_dups_list in entrez_dups_dict.items():
         df["entrez_gene_id"].replace(to_replace=entrez_dups_list, value=merged_entrez_id, inplace=True)
 
     df = df.fillna(-1).groupby(level=0).max()
     df.replace(-1, np.nan, inplace=True)
+    logger.trace(f"Shape after merging: {df.shape}")
 
     # TODO: Test if this is working properly
     """
@@ -192,8 +200,10 @@ async def _get_transcriptmoic_details(merged_df: pd.DataFrame, taxon_id: int) ->
     """
     # If _ExpressedHeaderNames.PROTEOMICS.value is in the dataframe, lower the required expression by 1
     # We are only trying to get details for transcriptomic data
+    logger.debug("Obtaining transcriptomic details")
     transcriptomic_df: pd.DataFrame = merged_df.copy()
     if _ExpressedHeaderNames.PROTEOMICS in merged_df.columns:
+        logger.trace("Proteomic data found, modifying required and total expression values")
         # Get the number of sources required for a gene to be marked "expressed"
         required_expression = merged_df["required"].iloc[0]
 
@@ -212,6 +222,7 @@ async def _get_transcriptmoic_details(merged_df: pd.DataFrame, taxon_id: int) ->
             ],
             inplace=False,
         )
+        logger.trace(f"Modified transcriptomic dataframe: {transcriptomic_df.shape}")
 
         # Must recalculate TotalExpressed because proteomic data was removed
         # If the TotalExpressed column is less than the Required column, set active to 1, otherwise set it to 0
@@ -226,6 +237,7 @@ async def _get_transcriptmoic_details(merged_df: pd.DataFrame, taxon_id: int) ->
         columns=["entrez_gene_id", "gene_symbol", "description", "gene_type"],
         index=list(range(len(transcriptomic_df))),
     )
+    logger.trace(f"Querying MyGene for details on {len(transcriptomic_df)} genes")
     for i, detail in enumerate(
         await my_gene.query(
             items=transcriptomic_df["entrez_gene_id"].tolist(),
@@ -237,6 +249,8 @@ async def _get_transcriptmoic_details(merged_df: pd.DataFrame, taxon_id: int) ->
         gene_details.at[i, "gene_symbol"] = detail["symbol"]
         gene_details.at[i, "description"] = detail["name"]
         gene_details.at[i, "gene_type"] = detail["type_of_gene"]
+
+    logger.debug("Finished obtaining transcriptomic details")
     return gene_details
 
 
@@ -254,6 +268,7 @@ async def _merge_xomics(
     force_activate_high_confidence: bool = True,
     adjust_for_missing_sources: bool = False,
 ):
+    logger.debug(f"Starting to merge data sources for context '{context_name}'")
     expression_list: list[str] = []
     high_confidence_list: list[str] = []
     merge_data = pd.DataFrame()
@@ -265,6 +280,7 @@ async def _merge_xomics(
         (proteomic_boolean_matrix, _ExpressedHeaderNames.PROTEOMICS, _HighExpressionHeaderNames.PROTEOMICS),
     ):
         if matrix is None:
+            logger.trace(f"Skipping {expressed_sourcetype} because it's matrix does not exist")
             continue
 
         matrix: pd.DataFrame  # re-define type to assist in type hinting for IDEs
@@ -273,18 +289,22 @@ async def _merge_xomics(
         matrix.rename(columns={"expressed": expressed_sourcetype, "high": high_expressed_sourcetype}, inplace=True)
         merge_data = matrix if merge_data.empty else merge_data.merge(matrix, on="entrez_gene_id", how="outer")
 
+    logger.trace(f"Shape of merged data before merging logical tables: {merge_data.shape}")
     if merge_data.empty:
-        logger.critical(
+        logger.warning(
             f"No data is available for the '{context_name}' context. If this is intentional, ignore this error."
         )
         return {}
 
     merge_data = _merge_logical_table(merge_data)
+    logger.debug(f"Shape of merged data after merging logical table: {merge_data.shape}")
     num_sources = len(expression_list)
     merge_data["active"] = 0
     merge_data["required"] = 0
 
+    logger.trace(f"Number of data sources: {num_sources}")
     if adjust_for_missing_sources:  # Subtract 1 from requirement per missing source
+        logger.trace("Adjusting for missing data sources")
         merge_data.loc[:, "required"] = merge_data[expression_list].apply(
             lambda x: expression_requirement - (num_sources - x.count())
             if (expression_requirement - (num_sources - x.count()) > 0)
@@ -292,22 +312,30 @@ async def _merge_xomics(
             axis=1,
         )
     else:  # Do not adjust for missing sources
+        logger.trace("Not adjusting for missing data sources")
         merge_data.loc[:, "required"] = merge_data[expression_list].apply(
             lambda x: expression_requirement if (expression_requirement - (num_sources - x.count()) > 0) else 1, axis=1
         )
+    logger.trace("Created expression requirement column")
 
     # Count the number of sources each gene is active in
     # set to active in final output if we meet the adjusted expression requirement
     merge_data["total_expressed"] = merge_data[expression_list].sum(axis=1)
     merge_data.loc[merge_data["total_expressed"] >= merge_data["required"], "active"] = 1
+    logger.trace("Created total expression requirement column")
 
     if force_activate_high_confidence:  # If a gene is high-confidence in at least 1 data source, set it to active
+        logger.trace("Forcing high confidence genes")
         merge_data.loc[merge_data[high_confidence_list].sum(axis=1) > 0, "active"] = 1
 
     merge_data.to_csv(output_merged_filepath, index=False)
+    logger.success(f"Saved merged data to {output_merged_filepath}")
+
+    logger.debug(f"Generating transcriptomic details using {output_merged_filepath}")
     transcriptomic_details = await _get_transcriptmoic_details(merge_data, taxon_id=taxon_id)
+    logger.debug(f"Saving transcriptomic details to {output_transcriptomic_details_filepath}")
     transcriptomic_details.to_csv(output_transcriptomic_details_filepath, index=False)
-
+    logger.success(f"Saved transcriptomic details to {output_transcriptomic_details_filepath}")
     return {context_name: output_gene_activity_filepath.as_posix()}
 
 
@@ -383,6 +411,7 @@ async def _process(
             weighted_z_floor=weighted_z_floor,
             weighted_z_ceiling=weighted_z_ceiling,
         )
+        logger.trace("Finished merging Z-Scores")
 
     # the more data sources available, the higher the expression requirement for the gene
     if adjust_method == AdjustmentMethod.PROGRESSIVE:
@@ -394,6 +423,7 @@ async def _process(
         adjusted_expression_requirement = expression_requirement - (4 - num_sources)
     elif adjust_method == AdjustmentMethod.FLAT:
         adjusted_expression_requirement = expression_requirement
+    logger.debug(f"Adjusted expression requirement: {adjusted_expression_requirement}")
 
     if adjusted_expression_requirement != expression_requirement:
         logger.debug(

From 9b520491185b70d2362f338d80a696795ee8fd7b Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:02:57 -0600
Subject: [PATCH 197/345] refactor: type hinting; drop na values

---
 main/como/merge_xomics.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 75b05be2..745cdca9 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -271,7 +271,7 @@ async def _merge_xomics(
     logger.debug(f"Starting to merge data sources for context '{context_name}'")
     expression_list: list[str] = []
     high_confidence_list: list[str] = []
-    merge_data = pd.DataFrame()
+    merge_data: pd.DataFrame = pd.DataFrame()
 
     for matrix, expressed_sourcetype, high_expressed_sourcetype in (
         (trna_boolean_matrix, _ExpressedHeaderNames.TRNASEQ, _HighExpressionHeaderNames.TRNASEQ),
@@ -328,12 +328,14 @@ async def _merge_xomics(
         logger.trace("Forcing high confidence genes")
         merge_data.loc[merge_data[high_confidence_list].sum(axis=1) > 0, "active"] = 1
 
+    merge_data.dropna(inplace=True)
     merge_data.to_csv(output_merged_filepath, index=False)
     logger.success(f"Saved merged data to {output_merged_filepath}")
 
     logger.debug(f"Generating transcriptomic details using {output_merged_filepath}")
     transcriptomic_details = await _get_transcriptmoic_details(merge_data, taxon_id=taxon_id)
     logger.debug(f"Saving transcriptomic details to {output_transcriptomic_details_filepath}")
+    transcriptomic_details.dropna(inplace=True)
     transcriptomic_details.to_csv(output_transcriptomic_details_filepath, index=False)
     logger.success(f"Saved transcriptomic details to {output_transcriptomic_details_filepath}")
     return {context_name: output_gene_activity_filepath.as_posix()}

From 38a9f1e74e3e02ecd6d1b7e15f2ffd08107dae49 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:03:16 -0600
Subject: [PATCH 198/345] feat: added function to update missing genome data

---
 main/como/merge_xomics.py | 45 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 45 insertions(+)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 745cdca9..b3f5025b 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -341,6 +341,51 @@ async def _merge_xomics(
     return {context_name: output_gene_activity_filepath.as_posix()}
 
 
+async def _update_missing_data(input_matrices: _InputMatrices, taxon_id: int) -> _InputMatrices:
+    logger.trace("Updating missing genomic data")
+    matrix_keys: dict[str, list[pd.DataFrame]] = {
+        "trna": [input_matrices.trna],
+        "mrna": [input_matrices.mrna],
+        "scrna": [input_matrices.scrna],
+        "proteomics": [input_matrices.proteomics],
+    }
+    logger.trace(f"Gathering missing data for data sources: {','.join(key for key in matrix_keys if key is not None)}")
+    # fmt: off
+    results = await asyncio.gather(
+        *[
+            # Using 'is not None' is required because the truth value of a Dataframe is ambiguous
+            get_missing_gene_data(values=input_matrices.trna, taxon_id=taxon_id) if input_matrices.trna is not None else asyncio.sleep(0),  # noqa: E501
+            get_missing_gene_data(values=input_matrices.mrna, taxon_id=taxon_id) if input_matrices.mrna is not None else asyncio.sleep(0),  # noqa: E501
+            get_missing_gene_data(values=input_matrices.scrna, taxon_id=taxon_id) if input_matrices.scrna is not None else asyncio.sleep(0),  # noqa: E501
+            get_missing_gene_data(values=input_matrices.proteomics, taxon_id=taxon_id) if input_matrices.proteomics is not None else asyncio.sleep(0),  # noqa: E501
+        ]
+    )
+    # fmt: on
+    for i, key in enumerate(matrix_keys):
+        matrix_keys[key].append(results[i])
+
+    for matrix_name, (matrix, conversion) in matrix_keys.items():
+        matrix: pd.DataFrame
+        if matrix is not None:
+            # fmt: off
+            existing_data = (
+                "gene_symbol" if "gene_symbol" in matrix
+                else "entrez_gene_id" if "entrez_gene_id" in matrix
+                else "ensembl_gene_id"
+            )
+            # fmt: on
+            logger.trace(f"Merging conversion data for {matrix_name}, existing id column is: {existing_data}")
+            input_matrices[matrix_name] = (
+                input_matrices[matrix_name]
+                .merge(conversion, how="left", on=[existing_data])
+                .dropna()
+                .reset_index(drop=True)
+            )
+
+    logger.debug("Updated missing genomic data")
+    return input_matrices
+
+
 async def _process(
     *,
     context_name: str,

From 11cd6b426fef4063da836ccd73437f42244882f7 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:04:53 -0600
Subject: [PATCH 199/345] refactor: build and use new parameter data types

---
 main/como/merge_xomics.py | 145 ++++++++++++++++++--------------------
 1 file changed, 70 insertions(+), 75 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index b3f5025b..b5483afe 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -389,22 +389,10 @@ async def _update_missing_data(input_matrices: _InputMatrices, taxon_id: int) ->
 async def _process(
     *,
     context_name: str,
-    trna_matrix: pd.DataFrame | None,
-    mrna_matrix: pd.DataFrame | None,
-    scrna_matrix: pd.DataFrame | None,
-    proteomic_matrix: pd.DataFrame | None,
-    trna_boolean_matrix: pd.DataFrame | None,
-    mrna_boolean_matrix: pd.DataFrame | None,
-    scrna_boolean_matrix: pd.DataFrame | None,
-    proteomic_boolean_matrix: pd.DataFrame | None,
-    trna_batches: dict[int, list[str]],
-    mrna_batches: dict[int, list[str]],
-    scrna_batches: dict[int, list[str]],
-    proteomic_batches: dict[int, list[str]],
-    trna_weight: int,
-    mrna_weight: int,
-    scrna_weight: int,
-    proteomic_weight: int,
+    input_matrices: _InputMatrices,
+    boolean_matrices: _InputMatrices,
+    batch_names: _BatchNames,
+    source_weights: _SourceWeights,
     taxon_id: int,
     minimum_source_expression: int,
     expression_requirement: int,
@@ -416,10 +404,7 @@ async def _process(
     adjust_for_missing_sources: bool,
     output_merge_activity_filepath: Path,
     output_transcriptomic_details_filepath: Path,
-    output_trna_activity_filepath: Path | None,
-    output_mrna_activity_filepath: Path | None,
-    output_scrna_activity_filepath: Path | None,
-    output_proteomic_activity_filepath: Path | None,
+    output_activity_filepaths: _OutputCombinedSourceFilepath,
     output_final_model_scores_filepath: Path,
     output_figure_dirpath: Path | None,
 ):
@@ -592,68 +577,81 @@ async def merge_xomics(  # noqa: C901
     if output_figure_dirpath:
         output_figure_dirpath.mkdir(parents=True, exist_ok=True)
 
-    # fmt: off
-    trna_matrix: pd.DataFrame | None = (
-        pd.read_csv(trna_matrix_or_filepath) if isinstance(trna_matrix_or_filepath, Path)
-        else trna_matrix_or_filepath if isinstance(trna_matrix_or_filepath, pd.DataFrame)
-        else None
-    )
-    mrna_matrix: pd.DataFrame | None = (
-        pd.read_csv(mrna_matrix_or_filepath) if isinstance(mrna_matrix_or_filepath, Path)
-        else mrna_matrix_or_filepath if isinstance(mrna_matrix_or_filepath, pd.DataFrame)
-        else None
+    # Build trna items
+    trna_matrix: pd.DataFrame | None
+    trna_boolean_matrix: pd.DataFrame | None
+    trna_metadata: pd.DataFrame | None
+    trna_matrix, trna_boolean_matrix, trna_metadata = await asyncio.gather(
+        *[
+            _read_file(trna_matrix_or_filepath),
+            _read_file(trna_boolean_matrix_or_filepath),
+            _read_file(trna_metadata_filepath_or_df),
+        ]
     )
-    scrna_matrix: pd.DataFrame | None = (
-        pd.read_csv(scrna_matrix_or_filepath) if isinstance(scrna_matrix_or_filepath, Path)
-        else scrna_matrix_or_filepath if isinstance(scrna_matrix_or_filepath, pd.DataFrame)
-        else None
+
+    # Build mrna items
+    mrna_matrix: pd.DataFrame | None
+    mrna_boolean_matrix: pd.DataFrame | None
+    mrna_metadata: pd.DataFrame | None
+    mrna_matrix, mrna_boolean_matrix, mrna_metadata = await asyncio.gather(
+        *[
+            _read_file(mrna_matrix_or_filepath),
+            _read_file(mrna_boolean_matrix_or_filepath),
+            _read_file(mrna_metadata_filepath_or_df),
+        ]
     )
-    proteomic_matrix: pd.DataFrame | None = (
-        pd.read_csv(proteomic_matrix_or_filepath) if isinstance(proteomic_matrix_or_filepath, Path)
-        else proteomic_matrix_or_filepath if isinstance(proteomic_matrix_or_filepath, pd.DataFrame)
-        else None
+
+    # build scrna items
+    scrna_matrix: pd.DataFrame | None
+    scrna_boolean_matrix: pd.DataFrame | None
+    scrna_metadata: pd.DataFrame | None
+    scrna_matrix, scrna_boolean_matrix, scrna_metadata = await asyncio.gather(
+        *[
+            _read_file(scrna_matrix_or_filepath),
+            _read_file(scrna_boolean_matrix_or_filepath),
+            _read_file(scrna_metadata_filepath_or_df),
+        ]
     )
 
-    trna_boolean_matrix: pd.DataFrame | None = (
-        pd.read_csv(trna_boolean_matrix_or_filepath) if isinstance(trna_boolean_matrix_or_filepath, Path)
-        else trna_boolean_matrix_or_filepath if isinstance(trna_boolean_matrix_or_filepath, pd.DataFrame)
-        else None
+    # build proteomic items
+    proteomic_matrix: pd.DataFrame | None
+    proteomic_boolean_matrix: pd.DataFrame | None
+    proteomic_metadata: pd.DataFrame | None
+    proteomic_matrix, proteomic_boolean_matrix, proteomic_metadata = await asyncio.gather(
+        *[
+            _read_file(proteomic_matrix_or_filepath),
+            _read_file(proteomic_boolean_matrix_or_filepath),
+            _read_file(proteomic_metadata_filepath_or_df),
+        ]
     )
-    mrna_boolean_matrix: pd.DataFrame | None = (
-        pd.read_csv(mrna_boolean_matrix_or_filepath) if isinstance(mrna_boolean_matrix_or_filepath, Path)
-        else mrna_boolean_matrix_or_filepath if isinstance(mrna_boolean_matrix_or_filepath, pd.DataFrame)
-        else None
+
+    source_weights = _SourceWeights(trna=trna_weight, mrna=mrna_weight, scrna=scrna_weight, proteomics=proteomic_weight)
+    input_matrices = _InputMatrices(trna=trna_matrix, mrna=mrna_matrix, scrna=scrna_matrix, proteomics=proteomic_matrix)
+    boolean_matrices = _InputMatrices(
+        trna=trna_boolean_matrix,
+        mrna=mrna_boolean_matrix,
+        scrna=scrna_boolean_matrix,
+        proteomics=proteomic_boolean_matrix,
     )
-    scrna_boolean_matrix: pd.DataFrame | None = (
-        pd.read_csv(scrna_boolean_matrix_or_filepath) if isinstance(scrna_boolean_matrix_or_filepath, Path)
-        else scrna_boolean_matrix_or_filepath if isinstance(scrna_boolean_matrix_or_filepath, pd.DataFrame)
-        else None
+    output_activity_filepaths = _OutputCombinedSourceFilepath(
+        trna=output_trna_activity_filepath,
+        mrna=output_mrna_activity_filepath,
+        scrna=output_scrna_activity_filepath,
+        proteomics=output_proteomic_activity_filepath,
     )
-    proteomic_boolean_matrix: pd.DataFrame | None = (
-        pd.read_csv(proteomic_boolean_matrix_or_filepath) if isinstance(proteomic_boolean_matrix_or_filepath, Path)
-        else proteomic_boolean_matrix_or_filepath if isinstance(proteomic_boolean_matrix_or_filepath, pd.DataFrame)
-        else None
+    batch_names = _build_batches(
+        trna_metadata=trna_metadata,
+        mrna_metadata=mrna_metadata,
+        scrna_metadata=scrna_metadata,
+        proteomic_metadata=proteomic_metadata,
     )
-    # fmt: on
 
     await _process(
         context_name=context_name,
-        trna_matrix=trna_matrix,
-        mrna_matrix=mrna_matrix,
-        scrna_matrix=scrna_matrix,
-        proteomic_matrix=proteomic_matrix,
-        trna_boolean_matrix=trna_boolean_matrix,
-        mrna_boolean_matrix=mrna_boolean_matrix,
-        scrna_boolean_matrix=scrna_boolean_matrix,
-        proteomic_boolean_matrix=proteomic_boolean_matrix,
-        trna_batches=trna_batches,
-        mrna_batches=mrna_batches,
-        scrna_batches=scrna_batches,
-        proteomic_batches=proteomic_batches,
-        trna_weight=trna_weight,
-        mrna_weight=mrna_weight,
-        scrna_weight=scrna_weight,
-        proteomic_weight=proteomic_weight,
+        input_matrices=input_matrices,
+        boolean_matrices=boolean_matrices,
+        source_weights=source_weights,
+        batch_names=batch_names,
         taxon_id=taxon_id,
         minimum_source_expression=minimum_source_expression,
         expression_requirement=expression_requirement,
@@ -663,12 +661,9 @@ async def merge_xomics(  # noqa: C901
         merge_zfpkm_distribution=merge_zfpkm_distribution,
         force_activate_high_confidence=force_activate_high_confidence,
         adjust_for_missing_sources=adjust_for_na,
+        output_activity_filepaths=output_activity_filepaths,
         output_merge_activity_filepath=output_merge_activity_filepath,
         output_transcriptomic_details_filepath=output_transcriptomic_details_filepath,
-        output_trna_activity_filepath=output_trna_activity_filepath,
-        output_mrna_activity_filepath=output_mrna_activity_filepath,
-        output_scrna_activity_filepath=output_scrna_activity_filepath,
-        output_proteomic_activity_filepath=output_proteomic_activity_filepath,
         output_final_model_scores_filepath=output_final_model_scores_filepath,
         output_figure_dirpath=output_figure_dirpath,
     )

From 0d484a2d53644f113f05bf619ff98f45037063e5 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:05:38 -0600
Subject: [PATCH 200/345] refactor: pass new parameter data types

---
 main/como/merge_xomics.py | 37 ++++++++++++-------------------------
 1 file changed, 12 insertions(+), 25 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index b5483afe..d2ff4045 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -410,34 +410,21 @@ async def _process(
 ):
     """Merge different data sources for each context type."""
     num_sources = sum(1 for source in [trna_matrix, mrna_matrix, scrna_matrix, proteomic_matrix] if source is not None)
+    logger.trace(
+        f"Settings: Min Expression: {minimum_source_expression}, Expression Requirement: {expression_requirement}, "
+        f"Weighted Z-Score Floor: {weighted_z_floor}, Weighted Z-Score Ceiling: {weighted_z_ceiling}, "
+        f"Adjust Method: {adjust_method.value}, Merge Z-Scores: {merge_zfpkm_distribution}, "
+        f"Force High Confidence: {force_activate_high_confidence}, Adjust for Missing: {adjust_for_missing_sources}"
+    )
 
     if merge_zfpkm_distribution:
-        _combine_zscores(
+        logger.trace("Merging Z-Scores")
+        await _begin_combining_distributions(
             context_name=context_name,
-            input_matrices=_InputMatrices(
-                trna=trna_matrix,
-                mrna=mrna_matrix,
-                scrna=scrna_matrix,
-                proteomics=proteomic_matrix,
-            ),
-            batch_names=_BatchNames(
-                trna=[_BatchEntry(batch_num=n, sample_names=s) for n, s in trna_batches.items()],
-                mrna=[_BatchEntry(batch_num=n, sample_names=s) for n, s in mrna_batches.items()],
-                scrna=[_BatchEntry(batch_num=n, sample_names=s) for n, s in scrna_batches.items()],
-                proteomics=[_BatchEntry(batch_num=n, sample_names=s) for n, s in proteomic_batches.items()],
-            ),
-            source_weights=_SourceWeights(
-                trna=trna_weight,
-                mrna=mrna_weight,
-                scrna=scrna_weight,
-                proteomics=proteomic_weight,
-            ),
-            output_filepaths=_OutputCombinedSourceFilepath(
-                trna=output_trna_activity_filepath,
-                mrna=output_mrna_activity_filepath,
-                scrna=output_scrna_activity_filepath,
-                proteomics=output_proteomic_activity_filepath,
-            ),
+            input_matrices=input_matrices,
+            batch_names=batch_names,
+            source_weights=source_weights,
+            output_filepaths=output_activity_filepaths,
             output_figure_dirpath=output_figure_dirpath,
             output_final_model_scores=output_final_model_scores_filepath,
             weighted_z_floor=weighted_z_floor,

From 07fc452c04061d0a6f4ab56b169de7998a4bea60 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:06:05 -0600
Subject: [PATCH 201/345] refactor: calculate variables near their usage

---
 main/como/merge_xomics.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index d2ff4045..5af5e63c 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -409,7 +409,6 @@ async def _process(
     output_figure_dirpath: Path | None,
 ):
     """Merge different data sources for each context type."""
-    num_sources = sum(1 for source in [trna_matrix, mrna_matrix, scrna_matrix, proteomic_matrix] if source is not None)
     logger.trace(
         f"Settings: Min Expression: {minimum_source_expression}, Expression Requirement: {expression_requirement}, "
         f"Weighted Z-Score Floor: {weighted_z_floor}, Weighted Z-Score Ceiling: {weighted_z_ceiling}, "
@@ -417,6 +416,10 @@ async def _process(
         f"Force High Confidence: {force_activate_high_confidence}, Adjust for Missing: {adjust_for_missing_sources}"
     )
 
+    # Collect missing genomic data for each of the input items in asynchronous parallel
+    input_matrices = await _update_missing_data(input_matrices, taxon_id)
+    logger.trace("Missing data updated")
+
     if merge_zfpkm_distribution:
         logger.trace("Merging Z-Scores")
         await _begin_combining_distributions(
@@ -433,6 +436,7 @@ async def _process(
         logger.trace("Finished merging Z-Scores")
 
     # the more data sources available, the higher the expression requirement for the gene
+    num_sources = sum(1 for source in input_matrices if source is not None)
     if adjust_method == AdjustmentMethod.PROGRESSIVE:
         adjusted_expression_requirement = (num_sources - minimum_source_expression) + expression_requirement
     # the more data sources available, the lower the expression requirement for the gene

From c371c124f49870e4421c3dc862639a836b97a118 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:06:24 -0600
Subject: [PATCH 202/345] style: more verbose variable name

---
 main/como/merge_xomics.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 5af5e63c..b13d6a7b 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -471,13 +471,14 @@ async def _process(
         )
         adjusted_expression_requirement = 1
 
+    logger.debug(f"Final Expression Requirement: {adjusted_expression_requirement}")
     await _merge_xomics(
         context_name=context_name,
         expression_requirement=adjusted_expression_requirement,
-        trna_boolean_matrix=trna_boolean_matrix,
-        mrna_boolean_matrix=mrna_boolean_matrix,
-        scrna_boolean_matrix=scrna_boolean_matrix,
-        proteomic_boolean_matrix=proteomic_boolean_matrix,
+        trna_boolean_matrix=boolean_matrices.trna,
+        mrna_boolean_matrix=boolean_matrices.mrna,
+        scrna_boolean_matrix=boolean_matrices.scrna,
+        proteomic_boolean_matrix=boolean_matrices.proteomics,
         output_merged_filepath=output_merge_activity_filepath,
         output_gene_activity_filepath=output_final_model_scores_filepath,
         output_transcriptomic_details_filepath=output_transcriptomic_details_filepath,

From 026f1cc3204a0526c1f6d7f9310341536ace53ff Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:06:39 -0600
Subject: [PATCH 203/345] feat: add functions for building batch data

---
 main/como/merge_xomics.py | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index b13d6a7b..6c74a1d5 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -488,6 +488,26 @@ async def _process(
     )
 
 
+def _build_batches(
+    trna_metadata: pd.DataFrame | None,
+    mrna_metadata: pd.DataFrame | None,
+    scrna_metadata: pd.DataFrame | None,
+    proteomic_metadata: pd.DataFrame | None,
+) -> _BatchNames:
+    batch_names = _BatchNames(**{source.name.lower(): [] for source in SourceTypes})
+    for source, metadata in zip(SourceTypes, [trna_metadata, mrna_metadata, scrna_metadata, proteomic_metadata]):
+        source: SourceTypes
+        metadata: pd.DataFrame
+        if metadata is None:
+            logger.trace(f"Metadata for source '{source.value}' is None, skipping")
+            continue
+
+        metadata: pd.DataFrame  # Re-assign type to assist in type hinting
+        for batch_num, study in enumerate(sorted(metadata["study"].unique()), start=1):
+            study_sample_names = metadata[metadata["study"] == study]["sample_name"].tolist()
+            batch_names[source.value].append(_BatchEntry(batch_num=batch_num, sample_names=study_sample_names))
+            logger.debug(f"Found {len(study_sample_names)} sample names for study '{study}', batch number {batch_num}")
+    return batch_names
 async def merge_xomics(  # noqa: C901
     context_name: str,
     trna_matrix_or_filepath: Path | pd.DataFrame | None,

From ebf3edd82290af7fc8289f519cb38d4374f39c70 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:06:55 -0600
Subject: [PATCH 204/345] feat: add function for validating data soruces

---
 main/como/merge_xomics.py | 25 +++++++++++++++++++++++++
 1 file changed, 25 insertions(+)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 6c74a1d5..8ba535eb 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -508,6 +508,31 @@ def _build_batches(
             batch_names[source.value].append(_BatchEntry(batch_num=batch_num, sample_names=study_sample_names))
             logger.debug(f"Found {len(study_sample_names)} sample names for study '{study}', batch number {batch_num}")
     return batch_names
+
+
+def _validate_source_arguments(
+    source: SourceTypes,
+    *args,
+) -> None:
+    """Validate arguments for each source are valid.
+
+    If at least one input item is provided, validate that all required items are also present.
+
+    :param matrix_or_filepath: The gene count matrix or filepath
+    :param boolean_matrix_or_filepath: The boolean matrix of gene activities
+    :param metadata_filepath_or_df: Dataframe or filepath to sample metadata
+    :param output_activity_filepath: Output filepath location
+    :param source: Source type
+
+    """
+    if any(i for i in args) and not all(i for i in args):
+        _log_and_raise_error(
+            f"Must specify all or none of '{source.value}' arguments",
+            error=ValueError,
+            level=LogLevel.ERROR,
+        )
+
+
 async def merge_xomics(  # noqa: C901
     context_name: str,
     trna_matrix_or_filepath: Path | pd.DataFrame | None,

From 0f1d584dca49acf0ea45d12dae413c17c561d483 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:07:40 -0600
Subject: [PATCH 205/345] refactor!: provide None as default argument

For parameters that are not needed, None is a default argument
---
 main/como/merge_xomics.py | 32 ++++++++++++++++----------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 8ba535eb..f89d7c27 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -535,27 +535,27 @@ def _validate_source_arguments(
 
 async def merge_xomics(  # noqa: C901
     context_name: str,
-    trna_matrix_or_filepath: Path | pd.DataFrame | None,
-    mrna_matrix_or_filepath: Path | pd.DataFrame | None,
-    scrna_matrix_or_filepath: Path | pd.DataFrame | None,
-    proteomic_matrix_or_filepath: Path | pd.DataFrame | None,
-    trna_boolean_matrix_or_filepath: Path | pd.DataFrame | None,
-    mrna_boolean_matrix_or_filepath: Path | pd.DataFrame | None,
-    scrna_boolean_matrix_or_filepath: Path | pd.DataFrame | None,
-    proteomic_boolean_matrix_or_filepath: Path | pd.DataFrame | None,
-    trna_batches: dict[int, list[str]],
-    mrna_batches: dict[int, list[str]],
-    scrna_batches: dict[int, list[str]],
-    proteomic_batches: dict[int, list[str]],
     output_merge_activity_filepath: Path,
     output_transcriptomic_details_filepath: Path,
-    output_trna_activity_filepath: Path | None,
-    output_mrna_activity_filepath: Path | None,
-    output_scrna_activity_filepath: Path | None,
-    output_proteomic_activity_filepath: Path | None,
     output_final_model_scores_filepath: Path,
     output_figure_dirpath: Path | None,
     taxon_id: int,
+    trna_matrix_or_filepath: Path | pd.DataFrame | None = None,
+    mrna_matrix_or_filepath: Path | pd.DataFrame | None = None,
+    scrna_matrix_or_filepath: Path | pd.DataFrame | None = None,
+    proteomic_matrix_or_filepath: Path | pd.DataFrame | None = None,
+    trna_boolean_matrix_or_filepath: Path | pd.DataFrame | None = None,
+    mrna_boolean_matrix_or_filepath: Path | pd.DataFrame | None = None,
+    scrna_boolean_matrix_or_filepath: Path | pd.DataFrame | None = None,
+    proteomic_boolean_matrix_or_filepath: Path | pd.DataFrame | None = None,
+    trna_metadata_filepath_or_df: Path | pd.DataFrame | None = None,
+    mrna_metadata_filepath_or_df: Path | pd.DataFrame | None = None,
+    scrna_metadata_filepath_or_df: Path | pd.DataFrame | None = None,
+    proteomic_metadata_filepath_or_df: Path | pd.DataFrame | None = None,
+    output_trna_activity_filepath: Path | None = None,
+    output_mrna_activity_filepath: Path | None = None,
+    output_scrna_activity_filepath: Path | None = None,
+    output_proteomic_activity_filepath: Path | None = None,
     trna_weight: int = 1,
     mrna_weight: int = 1,
     scrna_weight: int = 1,

From f0a2aab1c55770f43829378ebc51dd07f18f46ff Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:08:00 -0600
Subject: [PATCH 206/345] refactor: better building of source data variables

---
 main/como/merge_xomics.py | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index f89d7c27..61dc5580 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -583,17 +583,26 @@ async def merge_xomics(  # noqa: C901
                 proteomic_matrix_or_filepath,
             ]
         )
+    source_data = {
+        SourceTypes.TRNA: (trna_matrix_or_filepath, trna_boolean_matrix_or_filepath, trna_metadata_filepath_or_df, output_trna_activity_filepath),  # noqa: E501
+        SourceTypes.MRNA: (mrna_matrix_or_filepath, mrna_boolean_matrix_or_filepath, mrna_metadata_filepath_or_df, output_mrna_activity_filepath),  # noqa: E501
+        SourceTypes.SCRNA: (scrna_matrix_or_filepath, scrna_boolean_matrix_or_filepath, scrna_metadata_filepath_or_df, output_scrna_activity_filepath),  # noqa: E501
+        SourceTypes.PROTEOMICS: (proteomic_matrix_or_filepath, proteomic_boolean_matrix_or_filepath, proteomic_metadata_filepath_or_df, output_proteomic_activity_filepath),  # noqa: E501
+    }
+    # fmt: on
+    for source in source_data:
+        _validate_source_arguments(source, source_data[source])
 
     if all(
         file is None
-        for file in [
+        for file in (
             trna_matrix_or_filepath,
             mrna_matrix_or_filepath,
             scrna_matrix_or_filepath,
             proteomic_matrix_or_filepath,
-        ]
+        )
     ):
-        raise ValueError("No data was passed!")
+        _log_and_raise_error("No data was passed!", error=ValueError, level=LogLevel.ERROR)
 
     if adjust_method not in AdjustmentMethod:
         raise ValueError(f"Adjustment method must be one of {AdjustmentMethod}; got: {adjust_method}")

From b9bade66cf0c46dee7868d4f673667dc183b1407 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:08:20 -0600
Subject: [PATCH 207/345] feat: allow providing log levela and location

---
 main/como/merge_xomics.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 61dc5580..601255b2 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -568,10 +568,14 @@ async def merge_xomics(  # noqa: C901
     merge_zfpkm_distribution: bool = False,
     weighted_z_floor: int = -6,
     weighted_z_ceiling: int = 6,
+    log_level: LogLevel = LogLevel.INFO,
+    log_location: str | TextIOWrapper = sys.stderr,
 ):
     """Merge expression tables of multiple sources (RNA-seq, proteomics) into one."""
     if expression_requirement < 1:
         raise ValueError("Expression requirement must be at least 1!")
+    _set_up_logging(level=log_level, location=log_location)
+    logger.info(f"Starting to merge all omics data for context: '{context_name}'")
 
     if expression_requirement is None:
         expression_requirement = sum(

From 325b559a3f388750e592c4a61b3eb5f84c9b9be1 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:08:40 -0600
Subject: [PATCH 208/345] refactor: move variable creation closer to usage

---
 main/como/merge_xomics.py | 13 +------------
 1 file changed, 1 insertion(+), 12 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 601255b2..d507d683 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -572,21 +572,10 @@ async def merge_xomics(  # noqa: C901
     log_location: str | TextIOWrapper = sys.stderr,
 ):
     """Merge expression tables of multiple sources (RNA-seq, proteomics) into one."""
-    if expression_requirement < 1:
-        raise ValueError("Expression requirement must be at least 1!")
     _set_up_logging(level=log_level, location=log_location)
     logger.info(f"Starting to merge all omics data for context: '{context_name}'")
 
-    if expression_requirement is None:
-        expression_requirement = sum(
-            test is not None
-            for test in [
-                trna_matrix_or_filepath,
-                mrna_matrix_or_filepath,
-                scrna_matrix_or_filepath,
-                proteomic_matrix_or_filepath,
-            ]
-        )
+    # fmt: off
     source_data = {
         SourceTypes.TRNA: (trna_matrix_or_filepath, trna_boolean_matrix_or_filepath, trna_metadata_filepath_or_df, output_trna_activity_filepath),  # noqa: E501
         SourceTypes.MRNA: (mrna_matrix_or_filepath, mrna_boolean_matrix_or_filepath, mrna_metadata_filepath_or_df, output_mrna_activity_filepath),  # noqa: E501

From 463d54571d8aed775e63b5aff3b8eb13535edce9 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:09:03 -0600
Subject: [PATCH 209/345] refactor: use _log_and_raise_error

---
 main/como/merge_xomics.py | 24 +++++++++++++++++++++++-
 1 file changed, 23 insertions(+), 1 deletion(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index d507d683..a25f8e78 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -598,7 +598,29 @@ async def merge_xomics(  # noqa: C901
         _log_and_raise_error("No data was passed!", error=ValueError, level=LogLevel.ERROR)
 
     if adjust_method not in AdjustmentMethod:
-        raise ValueError(f"Adjustment method must be one of {AdjustmentMethod}; got: {adjust_method}")
+        _log_and_raise_error(
+            f"Adjustment method must be one of {AdjustmentMethod}; got: {adjust_method}",
+            error=ValueError,
+            level=LogLevel.ERROR,
+        )
+
+    if expression_requirement < 1:
+        logger.warning(
+            f"Expression requirement must be at least 1! Setting to the minimum of 1 now. Got: {expression_requirement}"
+        )
+        expression_requirement = 1
+
+    if expression_requirement is None:
+        expression_requirement = sum(
+            test is not None
+            for test in (
+                trna_matrix_or_filepath,
+                mrna_matrix_or_filepath,
+                scrna_matrix_or_filepath,
+                proteomic_matrix_or_filepath,
+            )
+        )
+        logger.debug(f"Expression requirement not specified; setting to {expression_requirement}")
 
     output_final_model_scores_filepath.parent.mkdir(parents=True, exist_ok=True)
     if output_merge_activity_filepath:

From f1c242d54b0bc0f161736718a755b75196c74b75 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:09:18 -0600
Subject: [PATCH 210/345] refactor: import required modules

---
 main/como/create_context_specific_model.py | 9 ++-------
 1 file changed, 2 insertions(+), 7 deletions(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index f6ac8b91..4fc95c79 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -1,12 +1,10 @@
 from __future__ import annotations
 
-import argparse
-import ast
 import collections
 import re
+import sys
 from collections.abc import Sequence
-from dataclasses import dataclass
-from enum import Enum
+from io import TextIOWrapper
 from pathlib import Path
 from typing import NamedTuple
 
@@ -22,9 +20,6 @@
 from troppo.methods.reconstruction.imat import IMAT, IMATProperties
 from troppo.methods.reconstruction.tINIT import tINIT, tINITProperties
 
-from como.project import Config
-from como.utils import Algorithm, Compartments, split_gene_expression_data, stringlist_to_list
-
 
 class Solver(Enum):
     """Solver used to seed context specific model."""

From 437082a0c91b20f05647528b64454b334d8c68ac Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:09:30 -0600
Subject: [PATCH 211/345] refactor!: move Solver to data_types

---
 main/como/create_context_specific_model.py | 25 ++--------------------
 1 file changed, 2 insertions(+), 23 deletions(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 4fc95c79..d0d44391 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -20,29 +20,8 @@
 from troppo.methods.reconstruction.imat import IMAT, IMATProperties
 from troppo.methods.reconstruction.tINIT import tINIT, tINITProperties
 
-
-class Solver(Enum):
-    """Solver used to seed context specific model."""
-
-    GLPK = "GLPK"
-    GUROBI = "GUROBI"
-    SCIPY = "SCIPY"
-    GLPK_EXACT = "GLPK_EXACT"
-
-    @staticmethod
-    def from_string(value: str) -> Solver:
-        """Convert string to Solver enum."""
-        match value.lower():
-            case "glpk":
-                return Solver.GLPK
-            case "gurobi":
-                return Solver.GUROBI
-            case "scipy":
-                return Solver.SCIPY
-            case "glpk_exact":
-                return Solver.GLPK_EXACT
-            case _:
-                raise ValueError(f"Unknown solver: {value}")
+from como.data_types import Algorithm, CobraCompartments, LogLevel, Solver
+from como.utils import _log_and_raise_error, _read_file, _set_up_logging, split_gene_expression_data
 
 
 class _BoundaryReactions(NamedTuple):

From 4694a5af64e14c397b513e2b8c621a32d6a922af Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:09:47 -0600
Subject: [PATCH 212/345] refactor: remove _Arguments cli parsing

---
 main/como/create_context_specific_model.py | 48 ----------------------
 1 file changed, 48 deletions(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index d0d44391..692b7f68 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -40,54 +40,6 @@ class _BuildResults(NamedTuple):
     infeasible_reactions: pd.DataFrame
 
 
-@dataclass
-class _Arguments:
-    """Arguments for creating a context specific model."""
-
-    context_name: str
-    reference_model: Path
-    active_genes_filepath: Path
-    objective: str
-    boundary_reactions_filepath: Path
-    exclude_reactions_filepath: Path
-    force_reactions_filepath: Path
-    recon_algorithm: Algorithm
-    solver: Solver
-    low_threshold: int
-    high_threshold: int
-    output_filetypes: list[str]
-
-    def __post_init__(self):
-        self.reference_model = Path(self.reference_model)
-        self.active_genes_filepath = Path(self.active_genes_filepath)
-        self.boundary_reactions_filepath = (
-            Path(self.boundary_reactions_filepath) if self.boundary_reactions_filepath is not None else None
-        )
-        self.exclude_reactions_filepath = (
-            Path(self.exclude_reactions_filepath) if self.exclude_reactions_filepath is not None else None
-        )
-        self.force_reactions_filepath = (
-            Path(self.force_reactions_filepath) if self.force_reactions_filepath is not None else None
-        )
-
-        if not self.reference_model.exists():
-            raise FileNotFoundError(f"Reference model not found at {self.reference_model}")
-        if not self.active_genes_filepath.exists():
-            raise FileNotFoundError(f"Active genes file not found at {self.active_genes_filepath}")
-        if self.boundary_reactions_filepath and not self.boundary_reactions_filepath.exists():
-            raise FileNotFoundError(f"Boundary reactions file not found at {self.boundary_reactions_filepath}")
-        if self.exclude_reactions_filepath and not self.exclude_reactions_filepath.exists():
-            raise FileNotFoundError(f"Exclude reactions file not found at {self.exclude_reactions_filepath}")
-        if self.force_reactions_filepath and not self.force_reactions_filepath.exists():
-            raise FileNotFoundError(f"Force reactions file not found at {self.force_reactions_filepath}")
-
-        if self.high_threshold < self.low_threshold:
-            raise ValueError(
-                f"Low threshold must be less than high threshold. "
-                f"Received low threshold: {self.low_threshold}, high threshold: {self.high_threshold}"
-            )
-
-
 def _correct_bracket(rule: str, name: str) -> str:
     """Correct GPR rules to format readable by."""
     rule_match = re.search(r"or|and", rule)

From 91a538fbed6b627bc999f62f49a0b4d01246000e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:10:18 -0600
Subject: [PATCH 213/345] fix: bracket and logical gene rule creation

---
 main/como/create_context_specific_model.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 692b7f68..96ca2cae 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -64,9 +64,9 @@ def _correct_bracket(rule: str, name: str) -> str:
         elif len(left_name) > 0 and char == left_name[0]:
             new_right_rule.append(char)
             left_name = left_name[1:]
-    new_left_rule = "".join(new_right_rule)
+    new_left_rule = "".join(new_right_rule).strip()
     final_right_rule = "" if rule_match is None else _correct_bracket(right_rule, right_name)
-    return " ".join([new_left_rule, operator, final_right_rule])
+    return " ".join([new_left_rule, operator, final_right_rule]).strip()
 
 
 def _gene_rule_logical(gpr_expression: str, level: int = 0) -> str:
@@ -75,10 +75,10 @@ def _gene_rule_logical(gpr_expression: str, level: int = 0) -> str:
         loc_r = gpr_expression.index(")")
     except ValueError:
         if "and" in gpr_expression:
-            gpr_expression = gpr_expression.replace("and", ",")
+            gpr_expression = gpr_expression.replace(" and ", ", ")
             return "min{" + gpr_expression + "}"
         elif "or" in gpr_expression:
-            gpr_expression = gpr_expression.replace("or", ",")
+            gpr_expression = gpr_expression.replace(" or ", ", ")
             return "max{" + gpr_expression + "}"
         else:
             gpr_expression = gpr_expression.replace("[", "")

From 675a26bb4e07a37ebc9822d543e85428ae75d980 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:10:46 -0600
Subject: [PATCH 214/345] refactor: reduce extraneous function usage

---
 main/como/create_context_specific_model.py | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 96ca2cae..91ed0c6b 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -104,14 +104,6 @@ def _gene_rule_logical(gpr_expression: str, level: int = 0) -> str:
     return expression_out
 
 
-def _gene_rule_evaluable(expression_in: str) -> str:
-    """Make expression rule evaluable."""
-    gene_reaction_by_rule = _gene_rule_logical(expression_in)
-    gene_reaction_by_rule = gene_reaction_by_rule.replace("{", "(")
-    gene_reaction_by_rule = gene_reaction_by_rule.replace("}", ")")
-
-    return gene_reaction_by_rule
-
 
 def _set_boundaries(model: cobra.Model, bound_rxns: list, bound_lb, bound_ub) -> tuple[cobra.Model, list]:
     all_rxns = model.reactions  # get all reactions

From 618edc731835f3d3058d129e1dd26a74509d1e09 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:10:57 -0600
Subject: [PATCH 215/345] style: ruff whitespace formatting

---
 main/como/create_context_specific_model.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 91ed0c6b..9d8e73b6 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -104,11 +104,12 @@ def _gene_rule_logical(gpr_expression: str, level: int = 0) -> str:
     return expression_out
 
 
-
-def _set_boundaries(model: cobra.Model, bound_rxns: list, bound_lb, bound_ub) -> tuple[cobra.Model, list]:
-    all_rxns = model.reactions  # get all reactions
-    bound_rm_rxns = []
-
+def _set_boundaries(
+    model: cobra.Model,
+    boundary_reactions: list[str],
+    lower_bounds: list[float],
+    upper_bounds: list[float],
+) -> cobra.Model:
     # get boundary reactions
     exchange_rxns = [rxn.id for rxn in all_rxns if re.search("EX_", rxn.id)]
     sink_rxns = [rxn.id for rxn in all_rxns if re.search("sink_", rxn.id)]

From 70371fee29caca2f382b4660340ad1e686eaae84 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:11:23 -0600
Subject: [PATCH 216/345] refactor: remove extraneous variable

---
 main/como/create_context_specific_model.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 9d8e73b6..c267587b 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -111,9 +111,9 @@ def _set_boundaries(
     upper_bounds: list[float],
 ) -> cobra.Model:
     # get boundary reactions
-    exchange_rxns = [rxn.id for rxn in all_rxns if re.search("EX_", rxn.id)]
-    sink_rxns = [rxn.id for rxn in all_rxns if re.search("sink_", rxn.id)]
-    demand_rxns = [rxn.id for rxn in all_rxns if re.search("DM_", rxn.id)]
+    exchange_rxns = [rxn.id for rxn in model.reactions if re.search("EX_", rxn.id)]
+    sink_rxns = [rxn.id for rxn in model.reactions if re.search("sink_", rxn.id)]
+    demand_rxns = [rxn.id for rxn in model.reactions if re.search("DM_", rxn.id)]
 
     # Allows all boundary reactions to be used if none are given
     allow_all_boundary_rxns = not bound_rxns

From cd36b5ed39489a0d563d0fcf1f376aa7c085b2ab Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:11:53 -0600
Subject: [PATCH 217/345] refactor: pythonic if-statement calculation

---
 main/como/create_context_specific_model.py | 17 +++++------------
 1 file changed, 5 insertions(+), 12 deletions(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index c267587b..7541d4c3 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -121,25 +121,18 @@ def _set_boundaries(
     # close sinks and demands not in boundary reactions unless no boundary reactions were given
     if not allow_all_boundary_rxns:
         for i, rxn in enumerate(sink_rxns):  # set sinks to 0
-            if rxn not in bound_rxns:  # only allow sink accumulation
-                getattr(model.reactions, rxn).lower_bounds = 0
-                getattr(model.reactions, rxn).upper_bounds = 1000
-            else:  # set from file
-                getattr(model.reactions, rxn).lower_bounds = bound_lb[i]
-                getattr(model.reactions, rxn).upper_bounds = bound_ub[i]
+            getattr(model.reactions, rxn).lower_bounds = lower_bounds[i] if rxn in boundary_reactions else 0
+            getattr(model.reactions, rxn).upper_bounds = upper_bounds[i] if rxn in boundary_reactions else 1000
 
         for i, rxn in enumerate(demand_rxns):
             getattr(model.reactions, rxn).lower_bounds = 0
-            getattr(model.reactions, rxn).upper_bounds = bound_ub[i] if rxn in bound_rxns else 0
+            getattr(model.reactions, rxn).upper_bounds = upper_bounds[i] if rxn in boundary_reactions else 0
 
     # Reaction media
     medium = model.medium  # get reaction media to modify
     for rxn in exchange_rxns:  # open exchanges from exchange file, close unspecified exchanges
-        if rxn not in bound_rxns:
-            medium[rxn] = 0.0
-        else:
-            medium[rxn] = -float(bound_lb[bound_rxns.index(rxn)])
-    model.medium = medium  # set new media
+        medium[rxn] = -float(lower_bounds[boundary_reactions.index(rxn)]) if rxn in boundary_reactions else 0.0
+    model.medium = medium
 
     return model, bound_rm_rxns
 

From d5f1fdffba9f4665c0186f1529e43a828d427a58 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:12:14 -0600
Subject: [PATCH 218/345] refactor: remove extraneous comment + variables

---
 main/como/create_context_specific_model.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 7541d4c3..e80ddc4f 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -116,7 +116,7 @@ def _set_boundaries(
     demand_rxns = [rxn.id for rxn in model.reactions if re.search("DM_", rxn.id)]
 
     # Allows all boundary reactions to be used if none are given
-    allow_all_boundary_rxns = not bound_rxns
+    allow_all_boundary_rxns = not boundary_reactions
 
     # close sinks and demands not in boundary reactions unless no boundary reactions were given
     if not allow_all_boundary_rxns:
@@ -129,12 +129,12 @@ def _set_boundaries(
             getattr(model.reactions, rxn).upper_bounds = upper_bounds[i] if rxn in boundary_reactions else 0
 
     # Reaction media
-    medium = model.medium  # get reaction media to modify
+    medium = model.medium
     for rxn in exchange_rxns:  # open exchanges from exchange file, close unspecified exchanges
         medium[rxn] = -float(lower_bounds[boundary_reactions.index(rxn)]) if rxn in boundary_reactions else 0.0
     model.medium = medium
 
-    return model, bound_rm_rxns
+    return model
 
 
 def _feasibility_test(model_cobra: cobra.Model, step: str):

From b586e251e4e1d314df974920d7c00f07420a08fa Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:12:56 -0600
Subject: [PATCH 219/345] style: more verbose parameter names

---
 main/como/create_context_specific_model.py | 40 +++++++++++++---------
 1 file changed, 24 insertions(+), 16 deletions(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index e80ddc4f..3f62d9ef 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -165,7 +165,7 @@ def _feasibility_test(model_cobra: cobra.Model, step: str):
     return incon_rxns, model_cobra_rm
 
 
-def _build_with_gimme(cobra_model, s_matrix, lb, ub, idx_objective, expr_vector):
+def _build_with_gimme(cobra_model, s_matrix, lower_bounds, upper_bounds, idx_objective, expr_vector):
     # `Becker and Palsson (2008). Context-specific metabolic networks are
     # consistent with experiments. PLoS Comput. Biol. 4, e1000082.`
     properties = GIMMEProperties(
@@ -175,23 +175,23 @@ def _build_with_gimme(cobra_model, s_matrix, lb, ub, idx_objective, expr_vector)
         preprocess=True,
         flux_threshold=0.9,
     )
-    algorithm = GIMME(s_matrix, lb, ub, properties)
+    algorithm = GIMME(s_matrix, lower_bounds, upper_bounds, properties)
     gene_activity = algorithm.run()
     context_cobra_model = cobra_model.copy()
-    r_ids = [r.id for r in context_cobra_model.reactions]
-    to_remove_ids = [r_ids[r] for r in np.where(gene_activity == 0)[0]]
+    reaction_ids = [r.id for r in context_cobra_model.reactions]
+    to_remove_ids = [reaction_ids[r] for r in np.where(gene_activity == 0)[0]]
 
     context_cobra_model.remove_reactions(to_remove_ids, True)
-    r_ids = [r.id for r in context_cobra_model.reactions]
     psol = pfba(context_cobra_model)  # noqa: F841
+    # reaction_ids = [r.id for r in context_cobra_model.reactions]
     # psol = context_cobra_model.optimize()
-    # to_remove_ids = [r_ids[r] for r in np.where(abs(psol.fluxes) < 1e-8)[0]]
+    # to_remove_ids = [reaction_ids[r] for r in np.where(abs(psol.fluxes) < 1e-8)[0]]
     # context_cobra_model.remove_reactions(to_remove_ids, True)
 
     return context_cobra_model
 
 
-def _build_with_fastcore(cobra_model, s_matrix, lb, ub, exp_idx_list, solver):
+def _build_with_fastcore(cobra_model, s_matrix, lower_bounds, upper_bounds, exp_idx_list, solver):
     # 'Vlassis, Pacheco, Sauter (2014). Fast reconstruction of compact
     # context-specific metabolic network models. PLoS Comput. Biol. 10,
     # e1003424.'
@@ -202,7 +202,7 @@ def _build_with_fastcore(cobra_model, s_matrix, lb, ub, exp_idx_list, solver):
     logger.debug("Creating feasible model")
     incon_rxns, cobra_model = _feasibility_test(cobra_model, "other")
     properties = FastcoreProperties(core=exp_idx_list, solver=solver)
-    algorithm = FASTcore(s_matrix, lb, ub, properties)
+    algorithm = FASTcore(s_matrix, lower_bounds, upper_bounds, properties)
     context_rxns = algorithm.fastcore()
     context_cobra_model = cobra_model.copy()
     r_ids = [r.id for r in context_cobra_model.reactions]
@@ -215,22 +215,22 @@ def _build_with_fastcore(cobra_model, s_matrix, lb, ub, exp_idx_list, solver):
 def _build_with_imat(
     cobra_model: cobra.Model,
     s_matrix: npt.NDArray,
-    lb: Sequence[float],
-    ub: Sequence[float],
+    lower_bounds: Sequence[float],
+    upper_bounds: Sequence[float],
     expr_vector: npt.NDArray,
-    expr_thesh: tuple[float, float],
+    expr_thresh: tuple[float, float],
     force_gene_ids: Sequence[int],
     solver: str,
 ) -> (cobra.Model, pd.DataFrame):
     expr_vector = np.array(expr_vector)
     properties = IMATProperties(
         exp_vector=expr_vector,
-        exp_thresholds=expr_thesh,
+        exp_thresholds=expr_thresh,
         core=force_gene_ids,
         epsilon=0.01,
         solver=solver.upper(),
     )
-    algorithm = IMAT(s_matrix, np.array(lb), np.array(ub), properties)
+    algorithm = IMAT(s_matrix, np.array(lower_bounds), np.array(upper_bounds), properties)
     context_rxns: npt.NDArray = algorithm.run()
     fluxes: pd.Series = algorithm.sol.to_series()
     context_cobra_model = cobra_model.copy()
@@ -249,7 +249,15 @@ def _build_with_imat(
     return context_cobra_model, flux_df
 
 
-def _build_with_tinit(cobra_model: cobra.Model, s_matrix, lb, ub, expr_vector, solver, idx_force) -> Model:
+def _build_with_tinit(
+    cobra_model: cobra.Model,
+    s_matrix,
+    lower_bounds,
+    upper_bounds,
+    expr_vector,
+    solver,
+    idx_force,
+) -> Model:
     properties = tINITProperties(
         reactions_scores=expr_vector,
         solver=solver,
@@ -258,10 +266,10 @@ def _build_with_tinit(cobra_model: cobra.Model, s_matrix, lb, ub, expr_vector, s
         allow_excretion=False,
         no_reverse_loops=True,
     )
-    algorithm = tINIT(s_matrix, lb, ub, properties)
+    algorithm = tINIT(s_matrix, lower_bounds, upper_bounds, properties)
     algorithm.preprocessing()
     algorithm.build_problem()
-    raise NotImplementedError("tINIT is not yet implemented")
+    _log_and_raise_error("tINIT is not yet implemented.", error=NotImplementedError, level=LogLevel.CRITICAL)
 
 
 def _map_expression_to_reaction(

From 498e84a8c5810f9de91fcdce960dbc1d32bdf39f Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:13:08 -0600
Subject: [PATCH 220/345] refactor: async usage where possible

---
 main/como/create_context_specific_model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 3f62d9ef..241ee2ee 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -272,7 +272,7 @@ def _build_with_tinit(
     _log_and_raise_error("tINIT is not yet implemented.", error=NotImplementedError, level=LogLevel.CRITICAL)
 
 
-def _map_expression_to_reaction(
+async def _map_expression_to_reaction(
     model_cobra,
     gene_expression_file,
     recon_algorithm: Algorithm,

From 1668b35f31a5e0f7ddd257738176e867f35f133b Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:13:16 -0600
Subject: [PATCH 221/345] refactor: type hinting

---
 main/como/create_context_specific_model.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 241ee2ee..6bdb4eae 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -276,9 +276,9 @@ async def _map_expression_to_reaction(
     model_cobra,
     gene_expression_file,
     recon_algorithm: Algorithm,
-    low_thresh=None,
-    high_thresh=None,
-):
+    low_thresh: float,
+    high_thresh: float,
+) -> collections.OrderedDict[str, int]:
     """Map gene ids to a reaction based on GPR (gene to protein to reaction) association rules.
 
     These rules should be defined in the general genome-scale metabolic model

From f162b94ac7d73dff131873750d58a274f325be21 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:13:40 -0600
Subject: [PATCH 222/345] refactor: pythonic collection of gene activity

---
 main/como/create_context_specific_model.py | 23 +++++++++++-----------
 1 file changed, 12 insertions(+), 11 deletions(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 6bdb4eae..0bd84b99 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -283,17 +283,18 @@ async def _map_expression_to_reaction(
 
     These rules should be defined in the general genome-scale metabolic model
     """
-    expression_data = pd.read_csv(gene_expression_file)
-    gene_expressions = split_gene_expression_data(expression_data, recon_algorithm=recon_algorithm)
-    expression_rxns = collections.OrderedDict()
-
-    unknown_val = 1
-    if recon_algorithm in {Algorithm.IMAT, Algorithm.TINIT}:
-        unknown_val = np.mean([low_thresh, high_thresh])  # put unknowns in mid bin
-    elif recon_algorithm == Algorithm.GIMME:
-        unknown_val = -1
-    elif recon_algorithm == Algorithm.FASTCORE:
-        unknown_val = 0
+    gene_activity = split_gene_expression_data(await _read_file(gene_expression_file), recon_algorithm=recon_algorithm)
+    reaction_expression = collections.OrderedDict()
+
+    # fmt: off
+    # Define a default expression value if a gene ID is not found
+    default_expression = (
+        np.mean([low_thresh, high_thresh]) if recon_algorithm in {Algorithm.IMAT, Algorithm.TINIT}
+        else -1 if recon_algorithm in {Algorithm.GIMME}
+        else 0 if recon_algorithm in {Algorithm.FASTCORE}
+        else 1
+    )
+    # fmt: on
 
     error_count = 0
     for rxn in model_cobra.reactions:

From 52f335f2e609d18f714d6084aba1f2cf15f1c4b7 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:24:31 -0600
Subject: [PATCH 223/345] fix: update mapping process to be more readable

---
 main/como/create_context_specific_model.py | 583 +++++++++------------
 1 file changed, 261 insertions(+), 322 deletions(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 0bd84b99..3d123c40 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -298,46 +298,57 @@ async def _map_expression_to_reaction(
 
     error_count = 0
     for rxn in model_cobra.reactions:
+        rxn: cobra.Reaction
         gene_reaction_rule = _correct_bracket(rxn.gene_reaction_rule, rxn.gene_name_reaction_rule)
-        gene_ids = re.findall(r"\d+", gene_reaction_rule)
-        expression_rxns[rxn.id] = unknown_val
-        if gene_reaction_rule.strip() == "":
+        if gene_reaction_rule == "":
             continue
-        for gid in gene_ids:
-            if gid in gene_expressions.index:
-                rep_val = f' {gene_expressions.at[gid, "active"]} '
-            else:
-                rep_val = f" {unknown_val!s} "
-            gene_reaction_rule = f" {gene_reaction_rule} "  # pad white space to prevent gene matches inside floats
-            gene_reaction_rule = gene_reaction_rule.replace(f" {gid} ", rep_val, 1)
-        try:
-            gene_reaction_by_rule = _gene_rule_evaluable(gene_reaction_rule)
-            gene_reaction_by_rule = gene_reaction_by_rule.strip()
-            expression_rxns[rxn.id] = ast.literal_eval(gene_reaction_by_rule)
 
-        except BaseException:
+        gene_ids = re.findall(r"\d+", gene_reaction_rule)
+        reaction_expression[rxn.id] = default_expression
+        for gene_id in gene_ids:
+            activity = (
+                f"{gene_activity.at[gene_id, 'active']}"
+                if gene_id in gene_activity.index
+                else f"{default_expression!s}"
+            )
+            # replace gene_id with activity, using optional whitespace before and after the gene id
+            # Do not replace the whitespace (if it exists) before and after the gene ID
+            gene_reaction_rule = re.sub(
+                pattern=rf"(?<!\S){gene_id}(?!\S)",
+                repl=activity,
+                string=gene_reaction_rule,
+                count=1,
+            )
+
+        try:
+            # We are using eval here because ast.literal_eval is unable to process an evaluable such as `max(-4, -4)`
+            # This isn't ideal, but ultimately the only other option is writing and maintaining a custom parsing engine,
+            #   which is too much work to do.
+            evaluable_gene_rule = _gene_rule_logical(gene_reaction_rule).replace("{", "(").replace("}", ")")
+            reaction_expression[rxn.id] = eval(evaluable_gene_rule)  # noqa: S307
+        except ValueError:
             error_count += 1
 
-    logger.info(f"Mapped gene expression to reactions, found {error_count} error(s).")
-    expr_vector = np.array(list(expression_rxns.values()), dtype=float)
+    logger.debug(f"Mapped gene expression to reactions, found {error_count} error(s).")
+    expr_vector = np.array(list(reaction_expression.values()), dtype=float)
 
-    return expression_rxns, expr_vector
+    return reaction_expression
 
 
-def _build_model(  # noqa: C901
-    context_name: str,
+async def _build_model(  # noqa: C901
     general_model_file: Path,
     gene_expression_file: Path,
     recon_algorithm: Algorithm,
     objective: str,
-    bound_rxns: list[str],
-    exclude_rxns: list[str],
-    force_rxns: list[str],
-    bound_lb: list[float],
-    bound_ub: list[float],
+    boundary_reactions: list[str],
+    exclude_reactions: list[str],
+    force_reactions: list[str],
+    lower_bounds: list[float],
+    upper_bounds: list[float],
     solver: str,
     low_thresh: float,
     high_thresh: float,
+    output_flux_result_filepath: Path,
 ) -> _BuildResults:
     """Seed a context specific reference_model.
 
@@ -346,7 +357,6 @@ def _build_model(  # noqa: C901
     file. Metabolite exchange (media), sinks, and demands are determined from exchanges file. Reactions can also be
     force excluded even if they meet GPR association requirements using the force exclude file.
     """
-    config = Config()
     reference_model: cobra.Model
     match general_model_file.suffix:
         case ".mat":
@@ -356,188 +366,230 @@ def _build_model(  # noqa: C901
         case ".json":
             reference_model = cobra.io.load_json_model(general_model_file)
         case _:
-            raise NameError(
-                f"Reference reference_model format must be .xml, .mat, or .json, found '{general_model_file.suffix}'"
+            _log_and_raise_error(
+                f"Reference model format must be .xml, .mat, or .json; found '{general_model_file.suffix}'",
+                error=ValueError,
+                level=LogLevel.ERROR,
             )
 
-    reference_model.objective = {getattr(reference_model.reactions, objective): 1}  # set objective
-
-    if objective not in force_rxns:
-        force_rxns.append(objective)
+    reference_model.objective = {getattr(reference_model.reactions, objective): 1}
+    if objective not in force_reactions:
+        force_reactions.append(objective)
 
-    # set boundaries
-    reference_model, bound_rm_rxns = _set_boundaries(reference_model, bound_rxns, bound_lb, bound_ub)
-
-    # set solver
+    reference_model = _set_boundaries(reference_model, boundary_reactions, lower_bounds, upper_bounds)
     reference_model.solver = solver.lower()
 
     # check number of unsolvable reactions for reference model under media assumptions
-    # incon_rxns, cobra_model = _feasibility_test(cobra_model, "before_seeding")
-    incon_rxns = []
-
+    # inconsistent_reactions, cobra_model = _feasibility_test(cobra_model, "before_seeding")
+    inconsistent_reactions = []
     s_matrix = cobra.util.array.create_stoichiometric_matrix(reference_model, array_type="dense")
-    lb = []
-    ub = []
-    rx_names = []
+    lower_bounds = []
+    upper_bounds = []
+    reaction_ids = []
     for reaction in reference_model.reactions:
-        lb.append(reaction.lower_bound)
-        ub.append(reaction.upper_bound)
-        rx_names.append(reaction.id)
+        lower_bounds.append(reaction.lower_bound)
+        upper_bounds.append(reaction.upper_bound)
+        reaction_ids.append(reaction.id)
 
     # get expressed reactions
-    expression_rxns, expr_vector = _map_expression_to_reaction(
+    reaction_expression: collections.OrderedDict[str, int] = await _map_expression_to_reaction(
         reference_model,
         gene_expression_file,
         recon_algorithm,
         high_thresh=high_thresh,
         low_thresh=low_thresh,
     )
+    expression_vector: npt.NDArray[np.int32] = np.array(list(reaction_expression.values()), dtype=np.int32)
 
-    for rxn in force_rxns:
-        if rxn not in rx_names:
+    for rxn in force_reactions:
+        if rxn not in reaction_ids:
             logger.warning(
                 f"The force reaction '{rxn}' was not found in the general reference_model. "
                 f"Check BiGG, or the relevant database for your general reference_model, for synonyms."
             )
 
     # collect list of reactions that are infeasible but active in expression data or user defined
-    infeas_exp_rxns = []
-    infeas_force_rxns = []
-    infeas_exp_cnt = 0
-    infeas_force_cnt = 0
+    infeasible_expression_reactions = []
+    infeasible_force_reactions = []
 
-    for idx, rxn in enumerate(expression_rxns):
+    for i, rxn in enumerate(reaction_expression):
         # log reactions in expressed and force lists that are infeasible that the user may wish to review
-        if rxn in incon_rxns and expr_vector[idx] == 1:
-            infeas_exp_cnt += 1
-            infeas_exp_rxns.append(rxn)
-        if rxn in incon_rxns and rxn in force_rxns:
-            infeas_force_cnt += 1
-            infeas_force_rxns.append(rxn)
-
-        # make changes to expressed reactions base on user defined force/exclude reactions
-        # TODO: if not using bound reactions file, add two sets of exchange reactions to be put in either low or mid bin
-
-        if rxn in force_rxns:
-            expr_vector[idx] = high_thresh + 0.1 if recon_algorithm.value in {"TINIT", "IMAT"} else 1
-        if rxn in incon_rxns or rxn in exclude_rxns:
-            expr_vector[idx] = low_thresh - 0.1 if recon_algorithm.value in {"TINIT", "IMAT"} else 0
-
-    idx_obj = rx_names.index(objective)
-    idx_force = [rx_names.index(rxn) for rxn in force_rxns if rxn in rx_names]
-    exp_idx_list = [i for (i, val) in enumerate(expr_vector) if val > 0]  # type: ignore
-    exp_thresh = (low_thresh, high_thresh)
-
-    if recon_algorithm == Algorithm.GIMME:
-        context_model_cobra = _build_with_gimme(reference_model, s_matrix, lb, ub, idx_obj, expr_vector)
-    elif recon_algorithm == Algorithm.FASTCORE:
-        context_model_cobra = _build_with_fastcore(reference_model, s_matrix, lb, ub, exp_idx_list, solver)
-    elif recon_algorithm == Algorithm.IMAT:
-        context_model_cobra: cobra.Model
-        context_model_cobra, flux_df = _build_with_imat(
-            reference_model,
-            s_matrix,
-            lb,
-            ub,
-            expr_vector,
-            exp_thresh,
-            idx_force,
-            solver=solver,
-        )
-        imat_reactions = flux_df.rxn
-        model_reactions = [reaction.id for reaction in context_model_cobra.reactions]
-        reaction_intersections = set(imat_reactions).intersection(model_reactions)
-        flux_df = flux_df[~flux_df["rxn"].isin(reaction_intersections)]
-        flux_df.to_csv(config.data_dir / "results" / context_name / f"{recon_algorithm.value}_flux.csv")
-    elif recon_algorithm == Algorithm.TINIT:
-        context_model_cobra = _build_with_tinit(reference_model, s_matrix, lb, ub, expr_vector, solver, idx_force)
-
-    incon_rxns_cs = []
-    incon_df = pd.DataFrame({"general_infeasible_rxns": list(incon_rxns)})
-    infeas_exp_df = pd.DataFrame({"expressed_infeasible_rxns": infeas_exp_rxns})
-    infeas_force_df = pd.DataFrame({"infeasible_rxns_in_force_list": infeas_exp_rxns})
-    incon_df_cs = pd.DataFrame({"infeasible_rxns_from_seeding": list(incon_rxns_cs)})
-    infeasible_df = pd.concat([incon_df, infeas_exp_df, infeas_force_df, incon_df_cs], ignore_index=True, axis=1)
-    infeasible_df.columns = ["InfeasRxns", "ExpressedInfeasRxns", "ForceInfeasRxns", "ContextInfeasRxns"]
+        if rxn in inconsistent_reactions and expression_vector[i] == 1:
+            infeasible_expression_reactions.append(rxn)
+        if rxn in inconsistent_reactions and rxn in force_reactions:
+            infeasible_force_reactions.append(rxn)
+
+        if rxn in force_reactions:
+            expression_vector[i] = high_thresh + 0.1 if recon_algorithm in {Algorithm.TINIT, Algorithm.IMAT} else 1
+        if rxn in inconsistent_reactions or rxn in exclude_reactions:
+            expression_vector[i] = low_thresh - 0.1 if recon_algorithm in {Algorithm.TINIT, Algorithm.IMAT} else 0
+
+    objective_index = reaction_ids.index(objective)
+    force_reaction_indices = [reaction_ids.index(rxn) for rxn in force_reactions if rxn in reaction_ids]
+    expression_vector_indices = [i for (i, val) in enumerate(expression_vector) if val > 0]  # type: ignore
+    expression_threshold = (low_thresh, high_thresh)
+
+    match recon_algorithm:
+        case Algorithm.GIMME:
+            context_model_cobra = _build_with_gimme(
+                cobra_model=reference_model,
+                s_matrix=s_matrix,
+                lower_bounds=lower_bounds,
+                upper_bounds=upper_bounds,
+                idx_objective=objective_index,
+                expr_vector=expression_vector,
+            )
+        case Algorithm.FASTCORE:
+            context_model_cobra = _build_with_fastcore(
+                cobra_model=reference_model,
+                s_matrix=s_matrix,
+                lower_bounds=lower_bounds,
+                upper_bounds=upper_bounds,
+                exp_idx_list=expression_vector_indices,
+                solver=solver,
+            )
+        case Algorithm.IMAT:
+            context_model_cobra: cobra.Model
+            context_model_cobra, flux_df = _build_with_imat(
+                cobra_model=reference_model,
+                s_matrix=s_matrix,
+                lower_bounds=lower_bounds,
+                upper_bounds=upper_bounds,
+                expr_vector=expression_vector,
+                expr_thresh=expression_threshold,
+                force_gene_ids=force_reaction_indices,
+                solver=solver,
+            )
+            imat_reactions = flux_df.rxn
+            model_reactions = [reaction.id for reaction in context_model_cobra.reactions]
+            reaction_intersections = set(imat_reactions).intersection(model_reactions)
+            flux_df: pd.DataFrame = flux_df[~flux_df["rxn"].isin(reaction_intersections)]
+            flux_df.dropna(inplace=True)
+            flux_df.to_csv(output_flux_result_filepath)
+        case Algorithm.TINIT:
+            context_model_cobra = _build_with_tinit(
+                cobra_model=reference_model,
+                s_matrix=s_matrix,
+                lower_bounds=lower_bounds,
+                upper_bounds=upper_bounds,
+                expr_vector=expression_vector,
+                solver=solver,
+                idx_force=force_reaction_indices,
+            )
+        case _:
+            _log_and_raise_error(
+                (
+                    f"Reconstruction algorithm must be {Algorithm.GIMME.value}, "
+                    f"{Algorithm.FASTCORE.value}, {Algorithm.IMAT.value}, or {Algorithm.TINIT.value}. "
+                    f"Got: {recon_algorithm.value}"
+                ),
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
+
+    inconsistent_and_infeasible_reactions: pd.DataFrame = pd.concat(
+        [
+            pd.DataFrame({"infeasible_reactions": inconsistent_reactions}),
+            pd.DataFrame({"expressed_infeasible_reactions": infeasible_expression_reactions}),
+            pd.DataFrame({"infeasible_force_reactions": infeasible_force_reactions}),
+            pd.DataFrame({"infeasible_context_reactions": []}),
+        ],
+        ignore_index=True,
+        axis=0,
+    )
+    print(inconsistent_and_infeasible_reactions)
 
     return _BuildResults(
         model=context_model_cobra,
-        expression_index_list=exp_idx_list,
-        infeasible_reactions=infeasible_df,
+        expression_index_list=expression_vector_indices,
+        infeasible_reactions=inconsistent_and_infeasible_reactions,
     )
 
 
-def _create_df(path: Path) -> pd.DataFrame:
+async def _create_df(path: Path) -> pd.DataFrame:
     match path.suffix:
-        case ".csv":
-            df = pd.read_csv(path, header=0, sep=",")
-        case ".tsv":
-            df = pd.read_csv(path, header=0, sep="\t")
+        case ".csv" | ".tsv":
+            df = await _read_file(path, header=0, sep="," if path.suffix == ".csv" else "\t")
         case ".xlsx" | ".xls":
-            df = pd.read_excel(path, header=0)
+            df = await _read_file(path, header=0)
         case _:
-            raise FileNotFoundError(f"File not found! Must be a csv, tsv, or Excel file. Searching for: {path}")
+            _log_and_raise_error(
+                f"File not found! Must be a csv, tsv, or Excel file. Searching for: {path}",
+                error=FileNotFoundError,
+                level=LogLevel.ERROR,
+            )
     df.columns = [c.lower() for c in df.columns]
     return df
 
 
-def _collect_boundary_reactions(path: Path) -> _BoundaryReactions:
-    df = _create_df(path)
+async def _collect_boundary_reactions(path: Path) -> _BoundaryReactions:
+    df: pd.DataFrame = await _create_df(path)
     for column in df.columns:
         if column not in [
             "boundary",
-            "reaction",
             "abbreviation",
             "compartment",
             "minimum reaction rate",
             "maximum reaction rate",
         ]:
-            raise ValueError(
-                f"Boundary reactions file must have columns named 'Reaction', 'Abbreviation', 'Compartment', "
-                f"'Minimum Reaction Rate', and 'Maximum Reaction Rate'. Found: {column}"
+            _log_and_raise_error(
+                (
+                    f"Boundary reactions file must have columns named 'Reaction', 'Abbreviation', 'Compartment', "
+                    f"'Minimum Reaction Rate', and 'Maximum Reaction Rate'. Found: {column}"
+                ),
+                error=ValueError,
+                level=LogLevel.ERROR,
             )
 
     reactions: list[str] = [""] * len(df)
-    boundary_type: list[str] = df["reaction"].tolist()
-    reaction_abbreviation: list[str] = df["abbreviation"].tolist()
-    reaction_compartment: list[str] = df["compartment"].tolist()
-    lower_bound = df["minimum reaction rate"].tolist()
-    upper_bound = df["maximum reaction rate"].tolist()
+    boundary_type: list[str] = df["boundary"].tolist()
+    reaction_abbreviation: list[str] = df["abbreviation"].astype(str).tolist()
+    reaction_compartment: list[str] = df["compartment"].astype(str).tolist()
     boundary_map = {"exchange": "EX", "demand": "DM", "sink": "SK"}
     for i in range(len(boundary_type)):
         boundary: str = boundary_type[i].lower()
         if boundary not in boundary_map:
-            raise ValueError(f"Boundary reaction type must be 'Exchange', 'Demand', or 'Sink'. Found: {boundary[i]}")
+            _log_and_raise_error(
+                f"Boundary reaction type must be 'Exchange', 'Demand', or 'Sink'. Found: {boundary}",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
-        shorthand_compartment = Compartments.get(reaction_compartment[i])
+        shorthand_compartment = CobraCompartments.get_shorthand(reaction_compartment[i])
         reactions[i] = f"{boundary_map.get(boundary)}_{reaction_abbreviation[i]}[{shorthand_compartment}]"
 
     return _BoundaryReactions(
         reactions=reactions,
-        lower_bounds=lower_bound,
-        upper_bounds=upper_bound,
+        lower_bounds=df["minimum reaction rate"].tolist(),
+        upper_bounds=df["maximum reaction rate"].tolist(),
     )
 
 
-def _write_model_to_disk(
+async def _write_model_to_disk(
     model: cobra.Model,
     output_directory: Path,
     context_name: str,
     output_filetypes: list[str],
     algorithm: Algorithm,
 ) -> None:
+    output_directory.mkdir(parents=True, exist_ok=True)
     if "mat" in output_filetypes:
-        cobra.io.save_matlab_model(model, output_directory / f"{context_name}_SpecificModel_{algorithm.value}.mat")
-    if "xml" in output_filetypes:
-        cobra.io.write_sbml_model(model, output_directory / f"{context_name}_SpecificModel_{algorithm.value}.xml")
+        cobra.io.save_matlab_model(model, (output_directory / f"{context_name}_SpecificModel_{algorithm.value}.mat"))
     if "json" in output_filetypes:
-        cobra.io.save_json_model(model, output_directory / f"{context_name}_SpecificModel_{algorithm.value}.json")
+        cobra.io.save_json_model(model, (output_directory / f"{context_name}_SpecificModel_{algorithm.value}.json"))
+    if "xml" in output_filetypes:
+        cobra.io.write_sbml_model(model, (output_directory / f"{context_name}_SpecificModel_{algorithm.value}.xml"))
 
 
-def create_context_specific_model(  # noqa: C901
+async def create_context_specific_model(  # noqa: C901
     context_name: str,
     reference_model: Path,
-    genes_file: Path,
+    active_genes_filepath: Path,
+    output_infeasible_reactions_filepath: Path,
+    output_model_dirpath: Path,
+    output_flux_result_filepath: Path,
+    output_filetypes: list[str] | None = None,
+    output_fastcore_expression_index_filepath: Path | None = None,
     objective: str = "biomass_reaction",
     boundary_rxns_filepath: str | Path | None = None,
     exclude_rxns_filepath: str | Path | None = None,
@@ -546,233 +598,120 @@ def create_context_specific_model(  # noqa: C901
     low_threshold: float = -5,
     high_threshold: float = -3,
     solver: Solver = Solver.GLPK,
-    output_filetypes: list[str] | None = None,
+    log_level: LogLevel = LogLevel.INFO,
+    log_location: str | TextIOWrapper = sys.stderr,
 ):
     """Create a context-specific model using the provided data."""
+    _set_up_logging(level=log_level, location=log_location)
     if not reference_model.exists():
-        raise FileNotFoundError(f"Reference model not found at {reference_model}")
-    if not genes_file.exists():
-        raise FileNotFoundError(f"Active genes file not found at {genes_file}")
-    if output_filetypes is None:
-        output_filetypes = ["mat"]
+        _log_and_raise_error(
+            f"Reference model not found at {reference_model}",
+            error=FileNotFoundError,
+            level=LogLevel.ERROR,
+        )
+    if not active_genes_filepath.exists():
+        _log_and_raise_error(
+            f"Active genes file not found at {active_genes_filepath}",
+            error=FileNotFoundError,
+            level=LogLevel.ERROR,
+        )
+    if algorithm == Algorithm.FASTCORE and not output_fastcore_expression_index_filepath:
+        _log_and_raise_error(
+            "The fastcore expression index output filepath must be provided",
+            error=ValueError,
+            level=LogLevel.ERROR,
+        )
+    if boundary_rxns_filepath and not boundary_rxns_filepath.exists():
+        _log_and_raise_error(
+            f"Boundary reactions file not found at {boundary_rxns_filepath}",
+            error=FileNotFoundError,
+            level=LogLevel.ERROR,
+        )
 
+    output_filetypes = ["mat"] if output_filetypes is None else output_filetypes
     for output_type in output_filetypes:
         if output_type not in {"xml", "mat", "json"}:
-            raise ValueError(f"Output file type {output_type} not recognized. Must be one of: 'xml', 'mat', 'json'")
+            _log_and_raise_error(
+                f"Output file type {output_type} not recognized. Must be one of: 'xml', 'mat', 'json'",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
     if algorithm not in Algorithm:
-        raise ValueError(f"Algorithm {algorithm} not supported. Use one of {', '.join(a.value for a in Algorithm)}")
+        _log_and_raise_error(
+            f"Algorithm {algorithm} not supported. Use one of {', '.join(a.value for a in Algorithm)}",
+            error=ValueError,
+            level=LogLevel.ERROR,
+        )
 
     if solver not in Solver:
-        raise ValueError(f"Solver '{solver}' not supported. Use one of {', '.join(s.value for s in Solver)}")
+        _log_and_raise_error(
+            f"Solver '{solver}' not supported. Use one of {', '.join(s.value for s in Solver)}",
+            error=ValueError,
+            level=LogLevel.ERROR,
+        )
 
     if boundary_rxns_filepath:
-        boundary_reactions = _collect_boundary_reactions(boundary_rxns_filepath)
+        boundary_reactions = await _collect_boundary_reactions(boundary_rxns_filepath)
 
     exclude_rxns: list[str] = []
     if exclude_rxns_filepath:
         exclude_rxns_filepath: Path = Path(exclude_rxns_filepath)
-        df = _create_df(exclude_rxns_filepath)
+        df = await _create_df(exclude_rxns_filepath)
         if "abbreviation" not in df.columns:
-            raise ValueError("The exclude reactions file should have a single column with a header named Abbreviation")
+            _log_and_raise_error(
+                "The exclude reactions file should have a single column with a header named Abbreviation",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
         exclude_rxns = df["abbreviation"].tolist()
 
     force_rxns: list[str] = []
     if force_rxns_filepath:
         force_rxns_filepath: Path = Path(force_rxns_filepath)
-        df = _create_df(force_rxns_filepath)
+        df = await _create_df(force_rxns_filepath)
         if "abbreviation" not in df.columns:
-            raise ValueError("The force reactions file should have a single column with a header named Abbreviation")
+            _log_and_raise_error(
+                "The force reactions file should have a single column with a header named Abbreviation",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
         force_rxns = df["abbreviation"].tolist()
 
     logger.info(f"Creating '{context_name}' model using '{algorithm.value}' reconstruction and '{solver.value}' solver")
-    build_results: _BuildResults = _build_model(
-        context_name=context_name,
+    build_results: _BuildResults = await _build_model(
         general_model_file=reference_model,
-        gene_expression_file=genes_file,
+        gene_expression_file=active_genes_filepath,
         recon_algorithm=algorithm,
         objective=objective,
-        bound_rxns=boundary_reactions.reactions,
-        bound_lb=boundary_reactions.lower_bounds,
-        bound_ub=boundary_reactions.upper_bounds,
-        exclude_rxns=exclude_rxns,
-        force_rxns=force_rxns,
+        boundary_reactions=boundary_reactions.reactions,
+        exclude_reactions=exclude_rxns,
+        force_reactions=force_rxns,
+        lower_bounds=boundary_reactions.lower_bounds,
+        upper_bounds=boundary_reactions.upper_bounds,
         solver=solver.value.lower(),
         low_thresh=low_threshold,
         high_thresh=high_threshold,
+        output_flux_result_filepath=output_flux_result_filepath,
     )
 
-    config = Config()
-    build_results.infeasible_reactions.to_csv(
-        config.result_dir / context_name / f"{context_name}_infeasible_rxns.csv", index=False
-    )
+    build_results.infeasible_reactions.dropna(inplace=True)
+    build_results.infeasible_reactions.to_csv(output_infeasible_reactions_filepath, index=False)
 
     if algorithm == Algorithm.FASTCORE:
-        pd.DataFrame(build_results.expression_index_list).to_csv(
-            config.result_dir / context_name / f"{context_name}_core_rxns.csv", index=False
-        )
+        fastcore_df = pd.DataFrame(build_results.expression_index_list)
+        fastcore_df.dropna(inplace=True)
+        fastcore_df.to_csv(output_fastcore_expression_index_filepath, index=False)
 
-    output_directory = config.result_dir / context_name
-    _write_model_to_disk(
+    await _write_model_to_disk(
         model=build_results.model,
-        output_directory=output_directory,
+        output_directory=output_model_dirpath,
         context_name=context_name,
         output_filetypes=output_filetypes,
         algorithm=algorithm,
     )
 
-    logger.success(f"Saved output file to {output_directory}")
-    logger.info(f"Number of Genes: {len(build_results.model.genes):,}")
-    logger.info(f"Number of Metabolites: {len(build_results.model.metabolites):,}")
-    logger.info(f"Number of Reactions: {len(build_results.model.reactions):,}")
-
-
-def _parse_args():
-    parser = argparse.ArgumentParser(
-        prog="create_context_specific_model.py",
-        description="Seed a context-specific model from a list of expressed genes, a reference",
-        epilog="For additional help, please post questions/issues in the MADRID GitHub repo at "
-        "https://github.com/HelikarLab/MADRID or email babessell@gmail.com",
-    )
-    parser.add_argument(
-        "-n",
-        "--context-name",
-        type=str,
-        required=True,
-        dest="context_name",
-        help="Name of context or context used consistent with outputs of merge_xomics.py.",
-    )
-    parser.add_argument(
-        "-m",
-        "--reference-model-filepath",
-        type=str,
-        required=True,
-        dest="reference_model",
-        help="Name of Genome-scale metabolic model to seed the context model to. For example, the "
-        "GeneralModelUpdatedV2.mat, is a modified Recon3D model. We also provide iMM_madrid for mouse."
-        "OT can be .mat, .xml, or .json.",
-    )
-    parser.add_argument(
-        "-g",
-        "--active-genes-filepath",
-        type=str,
-        required=True,
-        dest="active_genes_filepath",
-        help="Path to logical table of active genes output from merge_xomics.py called "
-        "ActiveGenes_contextName_Merged.csv. Should be in the corresponding context/context folder "
-        "inside /main/data/results/contextName/. The json file output from the function using "
-        "the context of interest as the key can be used here.",
-    )
-    parser.add_argument(
-        "-o",
-        "--objective",
-        type=str,
-        default="biomass_reaction",
-        dest="objective",
-        help="Reaction ID of the objective function in the model. Generally a biomass function.",
-    )
-    parser.add_argument(
-        "-b",
-        "--boundary-reactions-filepath",
-        type=str,
-        default=None,
-        dest="boundary_reactions_filepath",
-        help="Path to file contains the exchange (media), sink, and demand reactions which "
-        "the model should use to fulfill the reactions governed by transcriptomic and proteomics "
-        "data inputs. It must be a csv or xlsx with three columns: Rxn, Lowerbound, Upperbound. If not "
-        "specified, MADRID will allow ALL BOUNDARY REACTIONS THAT ARE OPEN IN THE REFERENCE MODEL "
-        "TO BE USED!",
-    )
-    parser.add_argument(
-        "-x",
-        "--exclude-reactions-filepath",
-        type=str,
-        default=None,
-        dest="exclude_reactions_filepath",
-        help="Filepath to file that contains reactions which will be removed from active reactions "
-        "the model to use when seeding, even if considered active from transcriptomic and "
-        "proteomics data inputs. It must be a csv or xlsx with one column of reaction IDs consistent with "
-        "the reference model",
-    )
-    parser.add_argument(
-        "-f",
-        "--force-reactions-filepath",
-        type=str,
-        default=None,
-        dest="force_reactions_filepath",
-        help="Filepath to file that contains reactions which will be added to active reactions for "
-        "the model to use when seeding (unless it causes the model to be unsolvable), regardless "
-        "of results of transcriptomic and proteomics data inputs. It must be a csv or xlsx with one "
-        "column of reaction IDs consistent with the reference model",
-    )
-    parser.add_argument(
-        "-a",
-        "--algorithm",
-        type=str,
-        default="GIMME",
-        dest="recon_algorithm",
-        help="Algorithm used to seed context specific model to the Genome-scale model. "
-        "Can be either GIMME, FASTCORE, iMAT, or tINIT.",
-    )
-    parser.add_argument(
-        "-lt",
-        "--low-threshold",
-        type=float,
-        default=-5,
-        dest="low_threshold",
-        help="Low to mid bin cutoff for iMAT solution",
-    )
-    parser.add_argument(
-        "-ht",
-        "--high-threshold",
-        type=float,
-        default=-3,
-        dest="high_threshold",
-        help="Mid to high bin cutoff for iMAT solution",
-    )
-    parser.add_argument(
-        "-s",
-        "--solver",
-        type=str,
-        default="glpk",
-        dest="solver",
-        help="Solver used to seed model and attempt to solve objective. Default is GLPK, also takes "
-        "GUROBI but you must mount a container license to the Docker to use. An academic license "
-        "can be obtained for free. See the README on the Github or Dockerhub for information on "
-        "mounting this license.",
-    )
-    parser.add_argument(
-        "-t",
-        "--output-filetypes",
-        type=str,
-        nargs="+",
-        default="mat",
-        dest="output_filetypes",
-        help="Filetypes to save seeded model type. Can be either a string with one filetype such as "
-        "'xml' or multiple in the format \"['extension1', 'extension2', ... etc]\". If you want "
-        "to output in all 3 accepted formats,  would be: \"['mat', 'xml', 'json']\" "
-        "Note the outer quotes required to be interpreted by cmd. This a string, not a python list",
-    )
-    args = parser.parse_args()
-    args.output_filetypes = stringlist_to_list(args.output_filetypes)
-    args.solver = Solver.from_string(args.solver)  # type: ignore
-    args.recon_algorithm = Algorithm.from_string(args.recon_algorithm)  # type: ignore
-    return _Arguments(**vars(args))
-
-
-if __name__ == "__main__":
-    args = _parse_args()
-    create_context_specific_model(
-        context_name=args.context_name,
-        reference_model=args.reference_model,
-        genes_file=args.active_genes_filepath,
-        objective=args.objective,
-        boundary_rxns_filepath=args.boundary_reactions_filepath,
-        exclude_rxns_filepath=args.exclude_reactions_filepath,
-        force_rxns_filepath=args.force_reactions_filepath,
-        algorithm=args.recon_algorithm,
-        low_threshold=args.low_threshold,
-        high_threshold=args.high_threshold,
-        solver=args.solver,
-        output_filetypes=args.output_filetypes,
-    )
+    logger.success(f"Saved metabolic model for context '{context_name}' to {output_model_dirpath}")
+    logger.debug(f"Number of Genes: {len(build_results.model.genes):,}")
+    logger.debug(f"Number of Metabolites: {len(build_results.model.metabolites):,}")
+    logger.debug(f"Number of Reactions: {len(build_results.model.reactions):,}")

From eab0713be116ee0c8bf7fa9922b5f367294f89a7 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:24:49 -0600
Subject: [PATCH 224/345] refactor: import required modules

---
 main/como/combine_distributions.py | 21 ++++++++++++++-------
 1 file changed, 14 insertions(+), 7 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 595f4442..a09d0e8d 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -1,17 +1,24 @@
 from __future__ import annotations
 
-from collections.abc import Iterator
-from dataclasses import dataclass, fields
+import asyncio
 from pathlib import Path
-from typing import Literal, NamedTuple
 
+import aiofiles.os
 import numpy as np
-import numpy.typing as npt
 import pandas as pd
-
+from loguru import logger
+
+from como.data_types import (
+    GeneIdentifier,
+    SourceTypes,
+    _BatchEntry,
+    _BatchNames,
+    _CombineOmicsInput,
+    _InputMatrices,
+    _OutputCombinedSourceFilepath,
+    _SourceWeights,
+)
 from como.graph import z_score_distribution as graph_zscore_distribution
-from como.utils import _num_rows
-
 
 class _IterableDataclass:
     def __iter__(self) -> Iterator[str]:

From 23a01f02e017bf10161cb00b623c8427f34c7582 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:25:08 -0600
Subject: [PATCH 225/345] refactor: move dataclasses to data_types.py

---
 main/como/combine_distributions.py | 51 ++----------------------------
 1 file changed, 3 insertions(+), 48 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index a09d0e8d..f2b4425b 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -19,54 +19,9 @@
     _SourceWeights,
 )
 from como.graph import z_score_distribution as graph_zscore_distribution
-
-class _IterableDataclass:
-    def __iter__(self) -> Iterator[str]:
-        return iter(getattr(self, field.name) for field in fields(self))  # type: ignore
-
-
-class _BatchEntry(NamedTuple):
-    batch_num: int
-    sample_names: list[str]
-
-
-@dataclass
-class _InputMatrices(_IterableDataclass):
-    trna: Path | pd.DataFrame | None
-    mrna: Path | pd.DataFrame | None
-    scrna: Path | pd.DataFrame | None
-    proteomics: Path | pd.DataFrame | None
-
-
-@dataclass
-class _BatchNames(_IterableDataclass):
-    trna: list[_BatchEntry]
-    mrna: list[_BatchEntry]
-    scrna: list[_BatchEntry]
-    proteomics: list[_BatchEntry]
-
-
-@dataclass
-class _SourceWeights(_IterableDataclass):
-    trna: int
-    mrna: int
-    scrna: int
-    proteomics: int
-
-
-@dataclass
-class _OutputCombinedSourceFilepath(_IterableDataclass):
-    trna: Path | None
-    mrna: Path | None
-    scrna: Path | None
-    proteomics: Path | None
-
-
-@dataclass
-class _CombineOmicsInput:
-    z_score_matrix: pd.DataFrame
-    type: Literal["totalrna", "mrna", "scrna", "proteomics"]
-    weight: int
+from como.utils import (
+    _num_columns,
+)
 
 
 def _combine_batch_zdistro(

From a207fcc64c5d50d23f50e9c758325189a673d7c1 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:25:58 -0600
Subject: [PATCH 226/345] refactor: make async functions

---
 main/como/combine_distributions.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index f2b4425b..37c01f27 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -24,8 +24,7 @@
 )
 
 
-def _combine_batch_zdistro(
-    matrix: pd.DataFrame,
+async def _combine_z_distribution_for_batch(
     context_name: str,
     batch_num: int,
     output_png_filepath: Path,
@@ -73,8 +72,8 @@ def weighted_z(x: npt.NDArray[float], floor: int, ceiling: int) -> npt.NDArray[f
     return weighted_matrix
 
 
-def _combine_context_zdistro(
     matrix: pd.DataFrame,
+async def _combine_z_distribution_for_source(
     context_name: str,
     batch_num: int,
     num_replicates: int,
@@ -203,7 +202,7 @@ def weighted_z(
     return combined_z_matrix
 
 
-def _combine_zscores(
+async def _begin_combining_distributions(
     context_name: str,
     input_matrices: _InputMatrices,
     batch_names: _BatchNames,

From 5d918c5bea5aa18f2cfab631c8102db79e017a4a Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:26:57 -0600
Subject: [PATCH 227/345] refactor: simplify processing, use async

---
 main/como/combine_distributions.py | 84 ++++++++++--------------------
 1 file changed, 27 insertions(+), 57 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 37c01f27..1155eb2c 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -213,71 +213,41 @@ async def _begin_combining_distributions(
     weighted_z_floor: int = -6,
     weighted_z_ceiling: int = 6,
 ):
+    logger.info(f"Starting to combine z-scores for context '{context_name}'")
     output_figure_dirpath.mkdir(parents=True, exist_ok=True)
-    source_name: list[str] = ["totalrna", "mrna", "scrna", "proteomics"]
-    zscore_results: list[_CombineOmicsInput] = []
-    for matrix, source in zip(input_matrices, source_name):
-        matrix: pd.DataFrame | None = pd.read_csv(matrix) if isinstance(matrix, Path) else matrix
 
-        if matrix is not None:
-            if source == "totalrna":
-                batch_data = batch_names.trna
-                weight = source_weights.trna
-                output_filepath = output_filepaths.trna
-            elif source == "mrna":
-                batch_data = batch_names.mrna
-                weight = source_weights.mrna
-                output_filepath = output_filepaths.mrna
-            elif source == "scrna":
-                batch_data = batch_names.scrna
-                weight = source_weights.scrna
-                output_filepath = output_filepaths.scrna
-            elif source == "proteomics":
-                batch_data = batch_names.proteomics
-                weight = source_weights.proteomics
-                output_filepath = output_filepaths.proteomics
-            else:
-                raise ValueError(f"Invalid source; got '{source}', expected one of '{','.join(source_name)}'")
+    z_score_results: list[_CombineOmicsInput] = []
+    for source, matrix in input_matrices:
+        if matrix is None:
+            logger.trace(f"Source '{source.value}' is None, skipping")
+            continue
+        if source not in SourceTypes:
+            logger.critical(f"Invalid source; got '{source.value}', expected 'trna', 'mrna', 'scrna', or 'proteomics'.")
+            raise ValueError("Invalid source")
 
-            replicate_count: list[int] = []
-            merge_z_data = pd.DataFrame()
-
-            batch: _BatchEntry
-            for batch in batch_data:
-                replicate_count.append(len(batch.sample_names))
-
-                batch_df: pd.DataFrame = matrix[["ensembl_gene_id", *batch.sample_names]]
-                # graph.z_score_distribution(
-                #     batch_df,
-                #     title=f"Z-Score Distribution for {context_name} - batch #{batch.batch_num} - {source}",
-                #     output_png_filepath=output_figure_dirpath
-                #     / f"{source}_batch{batch.batch_num}_zscore_distribution.png",
-                # )
-                combine_z_matrix: pd.DataFrame = _combine_batch_zdistro(
-                    matrix=batch_df,
+        batch_results = await asyncio.gather(
+            *[
+                _combine_z_distribution_for_batch(
                     context_name=context_name,
-                    batch_num=batch.batch_num,
-                    output_png_filepath=(
-                        output_figure_dirpath
-                        / f"combined_{source}_{context_name}_batch{batch.batch_num}_distribution.png"
+                    batch=batch,
+                    matrix=matrix[[GeneIdentifier.ENSEMBL_GENE_ID.value, *batch.sample_names]],
+                    source=source,
+                    output_combined_matrix_filepath=(
+                        output_filepaths[source.value].parent
+                        / f"{context_name}_{source.value}_batch{batch.batch_num}_combined_z_distribution_.csv"
                     ),
+                    output_figure_dirpath=output_figure_dirpath,
                     weighted_z_floor=weighted_z_floor,
                     weighted_z_ceiling=weighted_z_ceiling,
                 )
-                combine_z_matrix.columns = ["ensembl_gene_id", batch.batch_num]
-                merge_z_data = (
-                    combine_z_matrix
-                    if merge_z_data.empty
-                    else pd.merge(merge_z_data, combine_z_matrix, on="ensembl_gene_id", how="outer")
-                )
-            combine_batches_zscore = _combine_context_zdistro(
-                matrix=merge_z_data,
-                context_name=context_name,
-                batch_num=batch.batch_num,
-                num_replicates=sum(replicate_count),
-                output_png_filepath=output_figure_dirpath / f"totalrna_{context_name}_combined_distribution.png",
-                weighted_z_floor=weighted_z_floor,
-                weighted_z_ceiling=weighted_z_ceiling,
+                for batch in batch_names[source.value]
+            ]
+        )
+
+        merged_batch_results = pd.DataFrame()
+        for df in batch_results:
+            merged_batch_results = (
+                df if merged_batch_results.empty else merged_batch_results.merge(df, on="ensembl_gene_id", how="outer")
             )
             zscore_results.append(_CombineOmicsInput(z_score_matrix=combine_batches_zscore, type=source, weight=weight))  # type: ignore
             combine_batches_zscore.to_csv(output_filepath, index=False)

From 97cc2a9bce70a28887609c4805fa0813df9da61f Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:28:00 -0600
Subject: [PATCH 228/345] refactor: use relevant data types

---
 main/como/combine_distributions.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 1155eb2c..f66bd253 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -26,10 +26,13 @@
 
 async def _combine_z_distribution_for_batch(
     context_name: str,
-    batch_num: int,
-    output_png_filepath: Path,
-    weighted_z_floor: int = -6,
-    weighted_z_ceiling: int = 6,
+    batch: _BatchEntry,
+    matrix: pd.DataFrame,
+    source: SourceTypes,
+    output_combined_matrix_filepath: Path,
+    output_figure_dirpath: Path,
+    weighted_z_floor: int,
+    weighted_z_ceiling: int,
 ) -> pd.DataFrame:
     def weighted_z(x: npt.NDArray[float], floor: int, ceiling: int) -> npt.NDArray[float]:
         result = np.sum(x) / np.sqrt(len(x))

From aabcb8fd7d246bb4755c57013de7c04a9cd52112 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:28:25 -0600
Subject: [PATCH 229/345] refactor: pythonic approach to z score calculation

---
 main/como/combine_distributions.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index f66bd253..6a251180 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -34,9 +34,6 @@ async def _combine_z_distribution_for_batch(
     weighted_z_floor: int,
     weighted_z_ceiling: int,
 ) -> pd.DataFrame:
-    def weighted_z(x: npt.NDArray[float], floor: int, ceiling: int) -> npt.NDArray[float]:
-        result = np.sum(x) / np.sqrt(len(x))
-        return np.clip(result, floor, ceiling)
 
     if _num_rows(matrix) < 2:
         return matrix
@@ -48,6 +45,10 @@ def weighted_z(x: npt.NDArray[float], floor: int, ceiling: int) -> npt.NDArray[f
         floor=weighted_z_floor,
         ceiling=weighted_z_ceiling,
     )
+    values = matrix.iloc[:, 1:].values
+    weighted_matrix = np.sum(values, axis=1) / np.sqrt(values.shape[1])
+    weighted_matrix = np.clip(weighted_matrix, weighted_z_floor, weighted_z_ceiling).astype(np.int8)
+
     merge_df = pd.concat([matrix, pd.Series(weighted_matrix, name="combined")], axis=1)
     weighted_matrix = pd.DataFrame(
         {

From 0a795e12684c2e8e44dcb93050a22e2e0b0e3bd4 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:28:41 -0600
Subject: [PATCH 230/345] refactor: async create directories

---
 main/como/combine_distributions.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 6a251180..59c11917 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -34,6 +34,12 @@ async def _combine_z_distribution_for_batch(
     weighted_z_floor: int,
     weighted_z_ceiling: int,
 ) -> pd.DataFrame:
+    await asyncio.gather(
+        *[
+            aiofiles.os.makedirs(output_combined_matrix_filepath.parent.as_posix(), exist_ok=True),
+            aiofiles.os.makedirs(output_figure_dirpath.as_posix(), exist_ok=True),
+        ]
+    )
 
     if _num_rows(matrix) < 2:
         return matrix

From fdf415fdb360146509e4526f6b614c6a72e817e6 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:29:06 -0600
Subject: [PATCH 231/345] refactor: move when early return happens

---
 main/como/combine_distributions.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 59c11917..4e984dec 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -41,7 +41,17 @@ async def _combine_z_distribution_for_batch(
         ]
     )
 
-    if _num_rows(matrix) < 2:
+    logger.trace(
+        f"Combining z-score distributions: batch #{batch.batch_num}, "
+        f"samples: {len(batch.sample_names)}, "
+        f"source: '{source.value}', "
+        f"context: '{context_name}'"
+    )
+    if _num_columns(matrix) < 2:
+        logger.trace(
+            f"A single sample exists for batch '{batch.batch_num}'. "
+            f"Returning as-is because no additional combining can be done"
+        )
         return matrix
 
     weighted_matrix = np.apply_along_axis(

From 8c1ef047d35ccc40bf5d37f069c0341984d84088 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:29:18 -0600
Subject: [PATCH 232/345] fix: remove unused z score calculation

---
 main/como/combine_distributions.py | 7 -------
 1 file changed, 7 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 4e984dec..2e763268 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -54,13 +54,6 @@ async def _combine_z_distribution_for_batch(
         )
         return matrix
 
-    weighted_matrix = np.apply_along_axis(
-        weighted_z,
-        axis=1,
-        arr=matrix.iloc[:, 1:].values,
-        floor=weighted_z_floor,
-        ceiling=weighted_z_ceiling,
-    )
     values = matrix.iloc[:, 1:].values
     weighted_matrix = np.sum(values, axis=1) / np.sqrt(values.shape[1])
     weighted_matrix = np.clip(weighted_matrix, weighted_z_floor, weighted_z_ceiling).astype(np.int8)

From 60eb969ec95d8eafdd3409d563b653cb5146d1f3 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:29:29 -0600
Subject: [PATCH 233/345] fix: store gene ids as string

---
 main/como/combine_distributions.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 2e763268..d0f89d2a 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -61,7 +61,7 @@ async def _combine_z_distribution_for_batch(
     merge_df = pd.concat([matrix, pd.Series(weighted_matrix, name="combined")], axis=1)
     weighted_matrix = pd.DataFrame(
         {
-            "ensembl_gene_id": matrix["ensembl_gene_id"].astype(str),
+            "ensembl_gene_id": matrix["ensembl_gene_id"],
             "combine_z": weighted_matrix,
         },
     )

From 9cf76c2d992569b9072fa0057fdfb1d40e038301 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:29:47 -0600
Subject: [PATCH 234/345] refactor: more efficient pandas melting

---
 main/como/combine_distributions.py | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index d0f89d2a..b0a0ef36 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -65,14 +65,13 @@ async def _combine_z_distribution_for_batch(
             "combine_z": weighted_matrix,
         },
     )
-
-    stack_df = pd.concat(
-        [
-            pd.DataFrame(
-                {"ensembl_gene_id": merge_df["ensembl_gene_id"], "zscore": merge_df[col].astype(float), "source": col}
-            )
-            for col in merge_df.columns[1:]
-        ]
+    stack_df = pd.melt(
+        merge_df,
+        id_vars=["ensembl_gene_id"],
+        # Get all columns except ensembl_gene_id
+        value_vars=[col for col in merge_df.columns if col not in GeneIdentifier._member_map_],
+        var_name="source",
+        value_name="zscore",
     )
     if len(stack_df["source"].unique()) > 10:
         stack_df = stack_df[stack_df["source"] == "combined"]

From c7d147764708aac385275f00a26e0135b962800e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:29:57 -0600
Subject: [PATCH 235/345] fix: graph title name

---
 main/como/combine_distributions.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index b0a0ef36..7a1c2ceb 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -78,8 +78,9 @@ async def _combine_z_distribution_for_batch(
 
     graph_zscore_distribution(
         stack_df,
-        title=f"Combined Z-score Distribution for {context_name} - batch #{batch_num}",
-        output_png_filepath=output_png_filepath,
+        title=f"Combined Z-score Distribution for {context_name} - batch #{batch.batch_num}",
+        output_filepath=output_figure_dirpath
+        / f"{context_name}_{source.value}_batch{batch.batch_num}_combined_zscore_distribution.pdf",
     )
     return weighted_matrix
 

From 3e38a0f92aa6f2b1f9a9b1e55ee6636294f0b79b Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:30:17 -0600
Subject: [PATCH 236/345] refactor: add appropriate column names

---
 main/como/combine_distributions.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 7a1c2ceb..d46cbfac 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -82,10 +82,12 @@ async def _combine_z_distribution_for_batch(
         output_filepath=output_figure_dirpath
         / f"{context_name}_{source.value}_batch{batch.batch_num}_combined_zscore_distribution.pdf",
     )
+
+    weighted_matrix.columns = ["ensembl_gene_id", batch.batch_num]
+    weighted_matrix.to_csv(output_combined_matrix_filepath, index=False)
     return weighted_matrix
 
 
-    matrix: pd.DataFrame,
 async def _combine_z_distribution_for_source(
     context_name: str,
     batch_num: int,

From e323a9ab02b85b8fa4aa52dcc8a60d5ed636446b Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:30:40 -0600
Subject: [PATCH 237/345] refactor: rename parameter names

---
 main/como/combine_distributions.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index d46cbfac..6345e469 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -89,10 +89,11 @@ async def _combine_z_distribution_for_batch(
 
 
 async def _combine_z_distribution_for_source(
+    merged_source_data: pd.DataFrame,
     context_name: str,
-    batch_num: int,
     num_replicates: int,
-    output_png_filepath: Path,
+    output_combined_matrix_filepath: Path,
+    output_figure_filepath: Path,
     weighted_z_floor: int = -6,
     weighted_z_ceiling: int = 6,
 ):

From b575d40b17e898e0c9e9f920283a9b06067bdeff Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:31:26 -0600
Subject: [PATCH 238/345] refactor: log for early return

---
 main/como/combine_distributions.py | 35 +++++++++++++++++-------------
 1 file changed, 20 insertions(+), 15 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 6345e469..7c6cb1b1 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -97,21 +97,10 @@ async def _combine_z_distribution_for_source(
     weighted_z_floor: int = -6,
     weighted_z_ceiling: int = 6,
 ):
-    def weighted_z(
-        x: npt.NDArray[float],
-        n_reps: int,
-        floor: int,
-        ceiling: int,
-    ) -> npt.NDArray[float]:
-        na_values = np.where(np.isnan(x))[0]
-        if len(na_values) > 0:
-            x = np.delete(x, na_values)
-            n_reps = np.delete(n_reps, na_values)
-        weights = n_reps / np.sum(n_reps)
-        numerator = np.sum(weights * x)
-        denominator = np.sqrt(np.sum(weights**2))
-        result = numerator / denominator
-        return np.clip(result, floor, ceiling)
+    if _num_columns(merged_source_data) <= 2:
+        logger.warning("A single source exists, returning matrix as-is because no additional combining can be done")
+        merged_source_data.columns = ["ensembl_gene_id", "combine_z"]
+        return merged_source_data
 
     if _num_rows(matrix) < 2:
         matrix.columns = ["entrez_gene_id", "combine_z"]
@@ -141,6 +130,22 @@ def weighted_z(
         df=stack_df,
         title=f"Combined Z-score Distribution for {context_name} - batch #{batch_num}",
         output_png_filepath=output_png_filepath,
+    values = merged_source_data.iloc[:, 1:].values
+    mask = ~np.isnan(values)
+    masked_values = np.where(mask, values, 0)  # Replace NaN with 0
+    masked_num_replicates = np.where(mask, num_replicates, 0)
+
+    weights = masked_num_replicates / np.sum(masked_num_replicates, axis=1, keepdims=True)
+    numerator = np.sum(weights * masked_values, axis=1)
+    denominator = np.sqrt(np.sum(weights**2, axis=1))
+    weighted_matrix = numerator / denominator
+    weighted_matrix = np.clip(weighted_matrix, weighted_z_floor, weighted_z_ceiling)
+    merge_df = pd.concat([merged_source_data, pd.Series(weighted_matrix, name="combined")], axis=1)
+    weighted_matrix = pd.DataFrame(
+        {
+            "ensembl_gene_id": merged_source_data["ensembl_gene_id"],
+            "combine_z": weighted_matrix,
+        }
     )
     return weighted_matrix
 

From b2d7b2476e42013bb180c999642f4d78401d25de Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:31:43 -0600
Subject: [PATCH 239/345] feat: async create directories

---
 main/como/combine_distributions.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 7c6cb1b1..017059c8 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -124,6 +124,12 @@ async def _combine_z_distribution_for_source(
                 {"ensembl_gene_id": merge_df["ensembl_gene_id"], "zscore": merge_df[col].astype(float), "source": col}
                 for col in merge_df.columns[1:]
             )
+    print(f"Making directory for '{output_combined_matrix_filepath.parent}'")
+    print(f"Making directory for '{output_figure_filepath.parent}'")
+    await asyncio.gather(
+        *[
+            aiofiles.os.makedirs(output_combined_matrix_filepath.parent.as_posix(), exist_ok=True),
+            aiofiles.os.makedirs(output_figure_filepath.parent.as_posix(), exist_ok=True),
         ]
     )
     graph_zscore_distribution(

From d847da27d9a67695c9bc6351c87952a95855ed34 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:32:23 -0600
Subject: [PATCH 240/345] style: add logging

---
 main/como/combine_distributions.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 017059c8..e6f0fa60 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -136,6 +136,8 @@ async def _combine_z_distribution_for_source(
         df=stack_df,
         title=f"Combined Z-score Distribution for {context_name} - batch #{batch_num}",
         output_png_filepath=output_png_filepath,
+
+    logger.trace(f"Found {_num_columns(merged_source_data) - 1} samples for context '{context_name}' to combine")
     values = merged_source_data.iloc[:, 1:].values
     mask = ~np.isnan(values)
     masked_values = np.where(mask, values, 0)  # Replace NaN with 0
@@ -146,6 +148,7 @@ async def _combine_z_distribution_for_source(
     denominator = np.sqrt(np.sum(weights**2, axis=1))
     weighted_matrix = numerator / denominator
     weighted_matrix = np.clip(weighted_matrix, weighted_z_floor, weighted_z_ceiling)
+    logger.trace("Finished combining z-distribution")
     merge_df = pd.concat([merged_source_data, pd.Series(weighted_matrix, name="combined")], axis=1)
     weighted_matrix = pd.DataFrame(
         {

From 3a47f205638b94b36ff44f3f91cbf55e4703bb22 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:32:41 -0600
Subject: [PATCH 241/345] refactor: remove old z score calculation code

---
 main/como/combine_distributions.py | 26 --------------------------
 1 file changed, 26 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index e6f0fa60..0182af84 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -102,28 +102,6 @@ async def _combine_z_distribution_for_source(
         merged_source_data.columns = ["ensembl_gene_id", "combine_z"]
         return merged_source_data
 
-    if _num_rows(matrix) < 2:
-        matrix.columns = ["entrez_gene_id", "combine_z"]
-        return matrix
-
-    weighted_matrix = np.apply_along_axis(
-        weighted_z,
-        axis=1,
-        arr=matrix.iloc[:, 1:].values,
-        n_reps=num_replicates,
-        floor=weighted_z_floor,
-        ceiling=weighted_z_ceiling,
-    )
-    merge_df = pd.concat([matrix, pd.Series(weighted_matrix, name="combined")], axis=1)
-    weighted_matrix = pd.DataFrame(
-        {"ensembl_gene_id": matrix["ensembl_gene_id"].astype(str), "combine_z": weighted_matrix}
-    )
-    stack_df = pd.concat(
-        [
-            pd.DataFrame(
-                {"ensembl_gene_id": merge_df["ensembl_gene_id"], "zscore": merge_df[col].astype(float), "source": col}
-                for col in merge_df.columns[1:]
-            )
     print(f"Making directory for '{output_combined_matrix_filepath.parent}'")
     print(f"Making directory for '{output_figure_filepath.parent}'")
     await asyncio.gather(
@@ -132,10 +110,6 @@ async def _combine_z_distribution_for_source(
             aiofiles.os.makedirs(output_figure_filepath.parent.as_posix(), exist_ok=True),
         ]
     )
-    graph_zscore_distribution(
-        df=stack_df,
-        title=f"Combined Z-score Distribution for {context_name} - batch #{batch_num}",
-        output_png_filepath=output_png_filepath,
 
     logger.trace(f"Found {_num_columns(merged_source_data) - 1} samples for context '{context_name}' to combine")
     values = merged_source_data.iloc[:, 1:].values

From 229ea7c7089a32a571538f8bf9011e4d9d22ec21 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:32:56 -0600
Subject: [PATCH 242/345] refactor: better function name

---
 main/como/combine_distributions.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 0182af84..1e19e70f 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -133,10 +133,10 @@ async def _combine_z_distribution_for_source(
     return weighted_matrix
 
 
-def _combine_omics_zdistros(
+def _combine_z_distribution_for_context(
     context: str,
     zscore_results: list[_CombineOmicsInput],
-    output_png_filepath: Path,
+    output_graph_filepath: Path,
     weighted_z_floor: int = -6,
     weighted_z_ceiling: int = 6,
 ):

From 77c9ff1132f520fd7806e7bc4d23d41e7d72e748 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:33:49 -0600
Subject: [PATCH 243/345] refactor: pythonic z score calculation

---
 main/como/combine_distributions.py | 54 ++++++++++++------------------
 1 file changed, 22 insertions(+), 32 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 1e19e70f..12de7119 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -140,47 +140,37 @@ def _combine_z_distribution_for_context(
     weighted_z_floor: int = -6,
     weighted_z_ceiling: int = 6,
 ):
-    def weighted_z(
-        x: npt.NDArray[float],
-        weights,
-        floor: int,
-        ceiling: int,
-    ):
-        na_values = np.where(np.isnan(x))[0]
-        if len(na_values) > 0:
-            x = np.delete(x, na_values)
-            weights = np.delete(weights, na_values)
-        weights = weights / np.sum(weights)
-        numerator = np.sum(weights * x)
-        denominator = np.sqrt(np.sum(weights**2))
-        result = numerator / denominator
-        return np.clip(result, floor, ceiling)
 
-    z_matrix = pd.DataFrame()
-    for result in zscore_results:
-        result.z_score_matrix.columns = ["ensembl_gene_id", result.type]
-        z_matrix = (
-            result.z_score_matrix
-            if z_matrix.empty
-            else pd.merge(z_matrix, result.z_score_matrix, on="ensembl_gene_id", how="outer")
+    z_matrices = [
+        res.z_score_matrix.set_index("ensembl_gene_id").rename(
+            columns={col: res.type.value for col in res.z_score_matrix.columns[1:]}
         )
-
-    combined_z_matrix = (
-        np.apply_along_axis(
-            weighted_z,
-            axis=1,
-            arr=z_matrix.iloc[:, 1:].values,
-            weights=[r.weight for r in zscore_results],
-            floor=weighted_z_floor,
-            ceiling=weighted_z_ceiling,
+        for res in zscore_results
+    ]
+    z_matrix = pd.concat(z_matrices, axis=1, join="outer").reset_index()
+    if _num_columns(z_matrix) <= 1:
+        logger.trace(
+            f"Only 1 source exists for '{context}', returning dataframe as-is becuase no data exists to combine"
         )
         if _num_rows(z_matrix) > 2
         else z_matrix.iloc[:, 1:].values
     ).ravel()
     merge_df = pd.concat([z_matrix, pd.Series(combined_z_matrix, name="combined")], axis=1)
     combined_z_matrix = pd.DataFrame(
+    values = z_matrix.iloc[:, 1:].values
+    weights = np.array([r.weight for r in zscore_results])
+    mask = ~np.isnan(values)
+    masked_values = np.where(mask, values, 0)
+    masked_weights = np.where(mask, weights, 0)
+
+    normalized_weights = masked_weights / np.sum(masked_weights, axis=1, keepdims=True)
+    numerator = np.sum(normalized_weights * masked_values, axis=1)
+    denominator = np.sqrt(np.sum(normalized_weights**2, axis=1))
+    combined_z_matrix = numerator / denominator
+    combined_z_matrix = np.clip(combined_z_matrix, weighted_z_floor, weighted_z_ceiling)
+    combined_z_matrix_df = pd.DataFrame(
         {
-            "ensembl_gene_id": z_matrix["ensembl_gene_id"].astype(str),
+            "ensembl_gene_id": z_matrix["ensembl_gene_id"],
             "combine_z": combined_z_matrix,
         }
     )

From 3df7503c3311e4e39aa3e786dd668105b61af38d Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:33:56 -0600
Subject: [PATCH 244/345] style: log for early return

---
 main/como/combine_distributions.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 12de7119..88094c7a 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -140,6 +140,9 @@ def _combine_z_distribution_for_context(
     weighted_z_floor: int = -6,
     weighted_z_ceiling: int = 6,
 ):
+    if not zscore_results:
+        logger.warning("No zscore results exist, returning empty dataframe")
+        return pd.DataFrame({"ensembl_gene_id": [], "combine_z": []})
 
     z_matrices = [
         res.z_score_matrix.set_index("ensembl_gene_id").rename(

From 7a4140124d80b6ea89342836288ac99608cbd62a Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:34:38 -0600
Subject: [PATCH 245/345] refactor: remove old z score calculation code

---
 main/como/combine_distributions.py | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 88094c7a..5b1010d4 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -155,11 +155,9 @@ def _combine_z_distribution_for_context(
         logger.trace(
             f"Only 1 source exists for '{context}', returning dataframe as-is becuase no data exists to combine"
         )
-        if _num_rows(z_matrix) > 2
-        else z_matrix.iloc[:, 1:].values
-    ).ravel()
-    merge_df = pd.concat([z_matrix, pd.Series(combined_z_matrix, name="combined")], axis=1)
-    combined_z_matrix = pd.DataFrame(
+        z_matrix.columns = ["ensembl_gene_id", "combine_z"]
+        return z_matrix
+
     values = z_matrix.iloc[:, 1:].values
     weights = np.array([r.weight for r in zscore_results])
     mask = ~np.isnan(values)

From fc7b95b65a2894b5e8c966d3682f536f1e9fa6d4 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:39:08 -0600
Subject: [PATCH 246/345] refactor: more efficient pandas melting

---
 main/como/combine_distributions.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 5b1010d4..12b94924 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -130,6 +130,14 @@ async def _combine_z_distribution_for_source(
             "combine_z": weighted_matrix,
         }
     )
+
+    stack_df = pd.melt(
+        merge_df,
+        id_vars=["ensembl_gene_id"],
+        value_vars=merge_df.columns[1:],  # all other columns are values
+        var_name="source",
+        value_name="zscore",
+    )
     return weighted_matrix
 
 

From 78a1ab6b5c746cdf389e4bbc1be4d1fa8032cd2c Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:40:14 -0600
Subject: [PATCH 247/345] refactor: temporarily remove z score graphing

---
 main/como/combine_distributions.py | 55 ++++++++++++++++--------------
 1 file changed, 30 insertions(+), 25 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 12b94924..63218fca 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -76,12 +76,12 @@ async def _combine_z_distribution_for_batch(
     if len(stack_df["source"].unique()) > 10:
         stack_df = stack_df[stack_df["source"] == "combined"]
 
-    graph_zscore_distribution(
-        stack_df,
-        title=f"Combined Z-score Distribution for {context_name} - batch #{batch.batch_num}",
-        output_filepath=output_figure_dirpath
-        / f"{context_name}_{source.value}_batch{batch.batch_num}_combined_zscore_distribution.pdf",
-    )
+    # graph_zscore_distribution(
+    #     stack_df,
+    #     title=f"Combined Z-score Distribution for {context_name} - batch #{batch.batch_num}",
+    #     output_filepath=output_figure_dirpath
+    #     / f"{context_name}_{source.value}_batch{batch.batch_num}_combined_zscore_distribution.pdf",
+    # )
 
     weighted_matrix.columns = ["ensembl_gene_id", batch.batch_num]
     weighted_matrix.to_csv(output_combined_matrix_filepath, index=False)
@@ -102,8 +102,6 @@ async def _combine_z_distribution_for_source(
         merged_source_data.columns = ["ensembl_gene_id", "combine_z"]
         return merged_source_data
 
-    print(f"Making directory for '{output_combined_matrix_filepath.parent}'")
-    print(f"Making directory for '{output_figure_filepath.parent}'")
     await asyncio.gather(
         *[
             aiofiles.os.makedirs(output_combined_matrix_filepath.parent.as_posix(), exist_ok=True),
@@ -138,6 +136,11 @@ async def _combine_z_distribution_for_source(
         var_name="source",
         value_name="zscore",
     )
+    # graph_zscore_distribution(
+    #     df=stack_df,
+    #     title=f"Combined Z-score Distribution for {context_name}",
+    #     output_filepath=output_figure_filepath,
+    # )
     return weighted_matrix
 
 
@@ -184,25 +187,27 @@ def _combine_z_distribution_for_context(
         }
     )
 
-    stack_df = pd.concat(
-        [
-            pd.DataFrame(
-                {
-                    "ensembl_gene_id": merge_df["ensembl_gene_id"],
-                    "zscore": merge_df[col].astype(float),
-                    "source": col,
-                }
-            )
-            for col in merge_df.columns[1:]
-        ]
+    stack_df = pd.melt(
+        z_matrix,
+        id_vars=["ensembl_gene_id"],
+        value_vars=z_matrix.columns[1:],
+        var_name="source",
+        value_name="zscore",
     )
-
-    graph_zscore_distribution(
-        df=stack_df,
-        title=f"Combined Omics Z-score Distribution for {context}",
-        output_png_filepath=output_png_filepath,
+    combined_df = pd.DataFrame(
+        {
+            "ensembl_gene_id": z_matrix["ensembl_gene_id"],
+            "zscore": combined_z_matrix,
+            "source": "combined",
+        }
     )
-    return combined_z_matrix
+    stack_df = pd.concat([stack_df, combined_df])
+    # graph_zscore_distribution(
+    #     df=stack_df,
+    #     title=f"Combined Z-score Distribution for {context}",
+    #     output_filepath=output_graph_filepath,
+    # )
+    return combined_z_matrix_df
 
 
 async def _begin_combining_distributions(

From f34b1db9fbe1c4fd14e3114a2a82c3540c77e484 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:40:42 -0600
Subject: [PATCH 248/345] refactor: use new function names

---
 main/como/combine_distributions.py | 39 +++++++++++++++++++++++++-----
 1 file changed, 33 insertions(+), 6 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 63218fca..0096336d 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -257,12 +257,39 @@ async def _begin_combining_distributions(
             merged_batch_results = (
                 df if merged_batch_results.empty else merged_batch_results.merge(df, on="ensembl_gene_id", how="outer")
             )
-            zscore_results.append(_CombineOmicsInput(z_score_matrix=combine_batches_zscore, type=source, weight=weight))  # type: ignore
-            combine_batches_zscore.to_csv(output_filepath, index=False)
 
-    combined_z_omics = _combine_omics_zdistros(
+        merged_source_results: pd.DataFrame = await _combine_z_distribution_for_source(
+            merged_source_data=merged_batch_results,
+            context_name=context_name,
+            num_replicates=sum(batch.num_samples for batch in batch_names[source.value]),
+            output_combined_matrix_filepath=(
+                output_filepaths[source.value].parent
+                / f"{context_name}_{source.value}_combined_zscore_distribution.csv"
+            ),
+            output_figure_filepath=(
+                output_figure_dirpath / f"{context_name}_{source.value}_combined_zscore_distribution.pdf"
+            ),
+            weighted_z_floor=weighted_z_floor,
+            weighted_z_ceiling=weighted_z_ceiling,
+        )
+        z_score_results.append(
+            _CombineOmicsInput(
+                z_score_matrix=merged_source_results,
+                type=source,
+                weight=source_weights[source.value],
+            )
+        )
+        merged_source_results.to_csv(output_filepaths[source.value])
+        logger.success(
+            f"Wrote z-scores for source '{source.value}' "
+            f"in context '{context_name}' to '{output_filepaths[source.value]}'"
+        )
+
+    logger.trace(f"Combining z-score distributions for all sources in context '{context_name}'")
+    merged_context_results = _combine_z_distribution_for_context(
         context=context_name,
-        zscore_results=zscore_results,
-        output_png_filepath=output_figure_dirpath / f"{context_name}_combined_omics_distribution.png",
+        zscore_results=z_score_results,
+        output_graph_filepath=output_figure_dirpath / f"{context_name}_combined_omics_distribution.pdf",
     )
-    combined_z_omics.to_csv(output_final_model_scores, index=False)
+    merged_context_results.to_csv(output_final_model_scores, index=False)
+    logger.success(f"Finished combining z-scores for context '{context_name}'")

From 71ee8ccbe7af3ba95934ee55add99b566e42fcad Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:41:19 -0600
Subject: [PATCH 249/345] refactor: use matplotlib for plot creation

---
 main/como/graph/__init__.py | 47 ++++++++++++++++++++++---------------
 1 file changed, 28 insertions(+), 19 deletions(-)

diff --git a/main/como/graph/__init__.py b/main/como/graph/__init__.py
index 1bb4cdb8..1f6c6a22 100644
--- a/main/como/graph/__init__.py
+++ b/main/como/graph/__init__.py
@@ -1,7 +1,9 @@
 from pathlib import Path
 from loguru import logger
 import pandas as pd
-import plotly.express as px
+from matplotlib import pyplot as plt
+import seaborn as sns
+
 
 __all__ = ["z_score_distribution"]
 
@@ -9,27 +11,34 @@
 def z_score_distribution(
     df: pd.DataFrame,
     title: str,
-    output_png_filepath: Path,
+    output_filepath: Path,
 ):
-    if not output_png_filepath.suffix == ".png":
+    if output_filepath.suffix not in {".png", ".pdf", ".svg"}:
         logger.warning(
-            f"Expected .png suffix for output_png_filepath, got {output_png_filepath.suffix}. Defaulting to .png"
+            f"Expected .png, .pdf, or .svg suffix for output_png_filepath, got {output_filepath.suffix}. Defaulting to .pdf"
         )
-        output_png_filepath = output_png_filepath.with_suffix(".png")
-
-    fig = px.histogram(
-        df,
-        x="zscore",
-        color="source",
-        nbins=100,
-        marginal="rug",
-        title=title,
-    )
+        output_filepath = output_filepath.with_suffix(".pdf")
+    logger.trace(f"Graphing z-score distribution")
+    output_filepath.parent.mkdir(parents=True, exist_ok=True)
+    output_filepath.unlink(missing_ok=True)
 
-    fig.update_layout(xaxis_title="Z-score", yaxis_title="Frequency", font={"family": "sans-serif", "size": 12})
+    plt.figure(figsize=(10, 6))
 
-    # Simplified plot for many sources (optional)
-    if len(df["source"].unique()) > 10:
-        fig.update_layout(showlegend=False)
+    if len(df["source"].unique()) == 1:
+        ax = sns.histplot(df, x="zscore", bins=100, kde=True)
+        sns.rugplot(df, x="zscore", ax=ax)
+    else:
+        sns.histplot(df, x="zscore", hue="source", bins=100, kde=True, element="step")
+        plt.legend(loc="upper right", frameon=False, title=None)
 
-    fig.write_image(output_png_filepath)
+    plt.title(title)
+    plt.xlabel("Z-score")
+    plt.ylabel("Frequency")
+    plt.xticks(fontsize=12)
+    plt.yticks(fontsize=12)
+    plt.gca().spines["top"].set_visible(False)
+    plt.gca().spines["right"].set_visible(False)
+    plt.tight_layout()
+    plt.savefig(output_filepath)
+    plt.close()
+    logger.success(f"Saved z-score distribution graph to '{output_filepath}'")

From b6cf039c92958ff54c620800373c73cb1dcb566c Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:42:48 -0600
Subject: [PATCH 250/345] refactor: exchange plotly for seaborn

---
 pyproject.toml |  2 +-
 uv.lock        | 26 ++------------------------
 2 files changed, 3 insertions(+), 25 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 70ceb3e8..c6921422 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -17,10 +17,10 @@ dependencies = [
     "numpy<2.0",
     "openpyxl>=3.1.5",
     "pandas>=1.3.5",
-    "plotly>=5.24.1",
     "scanpy>=1.10.4",
     "scikit-learn>=1.5.2",
     "scipy>=1.7.3",
+    "seaborn>=0.13.2",
     "setuptools<60.0",
     "troppo@git+https://github.com/JoshLoecker/troppo@master",
 ]
diff --git a/uv.lock b/uv.lock
index 7d354a68..81292416 100644
--- a/uv.lock
+++ b/uv.lock
@@ -437,10 +437,10 @@ dependencies = [
     { name = "numpy" },
     { name = "openpyxl" },
     { name = "pandas" },
-    { name = "plotly" },
     { name = "scanpy" },
     { name = "scikit-learn" },
     { name = "scipy" },
+    { name = "seaborn" },
     { name = "setuptools" },
     { name = "troppo" },
 ]
@@ -481,7 +481,6 @@ requires-dist = [
     { name = "numpy", specifier = "<2.0" },
     { name = "openpyxl", specifier = ">=3.1.5" },
     { name = "pandas", specifier = ">=1.3.5" },
-    { name = "plotly", specifier = ">=5.24.1" },
     { name = "pytest", marker = "extra == 'dev'", specifier = ">=8.3.3" },
     { name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.24.0" },
     { name = "pytest-cov", marker = "extra == 'dev'", specifier = ">=6.0.0" },
@@ -489,6 +488,7 @@ requires-dist = [
     { name = "scanpy", specifier = ">=1.10.4" },
     { name = "scikit-learn", specifier = ">=1.5.2" },
     { name = "scipy", specifier = ">=1.7.3" },
+    { name = "seaborn", specifier = ">=0.13.2" },
     { name = "setuptools", specifier = "<60.0" },
     { name = "troppo", git = "https://github.com/JoshLoecker/troppo?rev=master" },
 ]
@@ -1825,19 +1825,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/3c/a6/bc1012356d8ece4d66dd75c4b9fc6c1f6650ddd5991e421177d9f8f671be/platformdirs-4.3.6-py3-none-any.whl", hash = "sha256:73e575e1408ab8103900836b97580d5307456908a03e92031bab39e4554cc3fb", size = 18439 },
 ]
 
-[[package]]
-name = "plotly"
-version = "5.24.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "packaging" },
-    { name = "tenacity" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/79/4f/428f6d959818d7425a94c190a6b26fbc58035cbef40bf249be0b62a9aedd/plotly-5.24.1.tar.gz", hash = "sha256:dbc8ac8339d248a4bcc36e08a5659bacfe1b079390b8953533f4eb22169b4bae", size = 9479398 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/e5/ae/580600f441f6fc05218bd6c9d5794f4aef072a7d9093b291f1c50a9db8bc/plotly-5.24.1-py3-none-any.whl", hash = "sha256:f67073a1e637eb0dc3e46324d9d51e2fe76e9727c892dde64ddf1e1b51f29089", size = 19054220 },
-]
-
 [[package]]
 name = "pluggy"
 version = "1.5.0"
@@ -2724,15 +2711,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/99/ff/c87e0622b1dadea79d2fb0b25ade9ed98954c9033722eb707053d310d4f3/sympy-1.13.3-py3-none-any.whl", hash = "sha256:54612cf55a62755ee71824ce692986f23c88ffa77207b30c1368eda4a7060f73", size = 6189483 },
 ]
 
-[[package]]
-name = "tenacity"
-version = "9.0.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/cd/94/91fccdb4b8110642462e653d5dcb27e7b674742ad68efd146367da7bdb10/tenacity-9.0.0.tar.gz", hash = "sha256:807f37ca97d62aa361264d497b0e31e92b8027044942bfa756160d908320d73b", size = 47421 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/b6/cb/b86984bed139586d01532a587464b5805f12e397594f19f931c4c2fbfa61/tenacity-9.0.0-py3-none-any.whl", hash = "sha256:93de0c98785b27fcf659856aa9f54bfbd399e29969b0621bc7f762bd441b4539", size = 28169 },
-]
-
 [[package]]
 name = "termcolor"
 version = "2.5.0"

From 4c424b5e19ece98dff1a1a05b2c34b9fde8aae98 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:43:18 -0600
Subject: [PATCH 251/345] feat: validate that source types process in order

---
 tests/unit/test_data_types.py | 10 ++++++++++
 1 file changed, 10 insertions(+)
 create mode 100644 tests/unit/test_data_types.py

diff --git a/tests/unit/test_data_types.py b/tests/unit/test_data_types.py
new file mode 100644
index 00000000..9688ddff
--- /dev/null
+++ b/tests/unit/test_data_types.py
@@ -0,0 +1,10 @@
+from como.data_types import SourceTypes
+
+
+def test_source_types():
+    """Validate that source types always go in the order of 'trna', 'mrna', 'scrna', 'protemics'."""
+    expected_order = ["trna", "mrna", "scrna", "proteomics"]
+    for expected, source in zip(expected_order, SourceTypes):
+        expected: str
+        source: SourceTypes
+        assert expected == source.value

From 22b82eff2b6bd95983ec9656a0f728bd73a3dbfd Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 15:48:29 -0600
Subject: [PATCH 252/345] refactor: add logging, use _log_and_raise_error

---
 main/como/proteomics/FTPManager.py            | 36 +++++++++++++++----
 main/como/proteomics/proteomics_preprocess.py | 14 +++++---
 2 files changed, 38 insertions(+), 12 deletions(-)

diff --git a/main/como/proteomics/FTPManager.py b/main/como/proteomics/FTPManager.py
index 1be45d91..d2807cc8 100644
--- a/main/como/proteomics/FTPManager.py
+++ b/main/como/proteomics/FTPManager.py
@@ -14,8 +14,11 @@
 from urllib.parse import urlparse
 
 import aioftp
+from loguru import logger
 
-from .FileInformation import FileInformation, clear_print
+from como.proteomics.FileInformation import FileInformation, clear_print
+from como.utils import _log_and_raise_error
+from como.data_types import LogLevel
 
 
 async def aioftp_client(
@@ -42,8 +45,11 @@ async def aioftp_client(
             attempt_num += 1
             time.sleep(5)
     if not connection_successful:
-        print()
-        raise ConnectionResetError("Could not connect to FTP server")
+        _log_and_raise_error(
+            "Could not connect to FTP server",
+            error=ConnectionResetError,
+            level=LogLevel.ERROR,
+        )
 
     return client
 
@@ -93,11 +99,19 @@ async def _get_info(self) -> None:
         if url_parse.hostname is not None:
             host = url_parse.hostname
         else:
-            raise ValueError(f"Unable to identify hostname from url: {self._root_link}")
+            _log_and_raise_error(
+                f"Unable to identify hostname from url: {self._root_link}",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
         if url_parse.path != "":
             folder = url_parse.path
         else:
-            raise ValueError(f"Unable to identify folder or path from url: {self._root_link}")
+            _log_and_raise_error(
+                f"Unable to identify folder or path from url: {self._root_link}",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         client = await aioftp_client(host=host)
         for path, info in await client.list(folder, recursive=True):
@@ -172,11 +186,19 @@ async def _aioftp_download_data(self, file_information: FileInformation, semapho
         if url_parse.hostname is not None:
             host = url_parse.hostname
         else:
-            raise ValueError(f"Unable to identify hostname from url: {file_information.download_url}")
+            _log_and_raise_error(
+                f"Unable to identify hostname from url: {file_information.download_url}",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
         if url_parse.path != "":
             folder = url_parse.path
         else:
-            raise ValueError(f"Unable to identify folder or path from url: {file_information.download_url}")
+            _log_and_raise_error(
+                f"Unable to identify folder or path from url: {file_information.download_url}",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         # Convert file size from byte to MB
         size_mb: int = round(file_information.file_size / (1024**2))
diff --git a/main/como/proteomics/proteomics_preprocess.py b/main/como/proteomics/proteomics_preprocess.py
index 89264ca2..7a7df11d 100644
--- a/main/como/proteomics/proteomics_preprocess.py
+++ b/main/como/proteomics/proteomics_preprocess.py
@@ -7,8 +7,9 @@
 
 from loguru import logger
 
-from . import Crux, FTPManager
-from .FileInformation import FileInformation
+from como.data_types import LogLevel
+from como.proteomics import Crux, FileInformation, FTPManager
+from como.utils import _log_and_raise_error
 
 
 class ArgParseFormatter(argparse.RawTextHelpFormatter, argparse.ArgumentDefaultsHelpFormatter):
@@ -308,14 +309,17 @@ def parse_args() -> argparse.Namespace:
 
     # Validte the input file exists
     if not Path(args.input_csv).is_file():
-        raise FileNotFoundError(f"Input file {args.input} does not exist!")
+        _log_and_raise_error(f"Input file {args.input} does not exist!", error=FileNotFoundError, level=LogLevel.ERROR)
 
     if args.core_count == "all":
         args.core_count = os.cpu_count()
     elif not str(args.core_count).isdigit():
-        raise ValueError(
-            f"Invalid option '{args.core_count}' for option '--cores'. Enter an integer or 'all' to use all cores"
+        _log_and_raise_error(
+            f"Invalid option '{args.core_count}' for option '--cores'. Enter an integer or 'all' to use all cores",
+            error=ValueError,
+            level=LogLevel.ERROR,
         )
+
     else:
         args.core_count = int(args.core_count)
         if args.core_count > os.cpu_count():

From 82ce6b3ea25031c738d485a87c61e3e86a8920f2 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 16:13:51 -0600
Subject: [PATCH 253/345] refactor: remove plotly graphing

---
 main/como/rnaseq_gen.py | 78 +++++++++++++++++++----------------------
 1 file changed, 37 insertions(+), 41 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 8fe2780a..6a570c26 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -11,17 +11,16 @@
 from pathlib import Path
 from typing import Callable, NamedTuple
 
+import matplotlib.pyplot as plt
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
-import plotly.graph_objs as go
-import scanpy as sc
+import seaborn as sns
 import sklearn
 import sklearn.neighbors
 from fast_bioservices.pipeline import ensembl_to_gene_id_and_symbol, gene_symbol_to_ensembl_and_gene_id
 from loguru import logger
 from pandas import DataFrame
-from plotly.subplots import make_subplots
 from scipy.signal import find_peaks
 from sklearn.neighbors import KernelDensity
 
@@ -412,10 +411,11 @@ def zfpkm_transform(
     return results, zfpkm_df
 
 
-def zfpkm_plot(results, *, plot_xfloor: int = -4, subplot_titles: bool = True):
+def zfpkm_plot(results, *, output_png_filepath: Path, plot_xfloor: int = -4, subplot_titles: bool = True):
     """Plot the log2(FPKM) density and fitted Gaussian for each sample.
 
     :param results: A dictionary of intermediate results from zfpkm_transform.
+    :param output_png_filepath: Output filepath location
     :param: subplot_titles: Whether to display facet titles (sample names).
     :param plot_xfloor: Lower limit for the x-axis.
     :param subplot_titles: Whether to display facet titles (sample names).
@@ -445,45 +445,30 @@ def zfpkm_plot(results, *, plot_xfloor: int = -4, subplot_titles: bool = True):
     mega_df.columns = pd.Series(data=["sample_name", "log2fpkm", "fpkm_density", "fitted_density_scaled"])
     mega_df = mega_df.melt(id_vars=["log2fpkm", "sample_name"], var_name="source", value_name="density")
 
-    subplot_titles = list(results.keys()) if subplot_titles else None
-    fig = make_subplots(
-        rows=len(results),
-        cols=1,
-        subplot_titles=subplot_titles,
-        vertical_spacing=min(0.05, (1 / (len(results) - 1))),
-    )
+    fig, axes = plt.subplot(len(results), 1, figsize=(8, 4 * len(results)))
+    if len(results) == 1:
+        axes = [axes]
 
     for i, sample_name in enumerate(results, start=1):
         sample_data = mega_df[mega_df["sample_name"] == sample_name]
-        traces = []
+        axis = axes[i]
+
         for source_type in sample_data["source"].unique():
             group = sample_data[sample_data["source"] == source_type]
-            traces.append(
-                go.Scatter(
-                    x=group["log2fpkm"],
-                    y=group["density"],
-                    mode="lines",
-                    name=source_type,
-                    legendgroup=source_type,
-                )
-            )
-        fig.add_traces(traces, rows=i, cols=1)
-        fig.update_xaxes(title_text="log2(FPKM)", range=[plot_xfloor, sample_data["log2fpkm"].max()], row=i, col=1)
-        fig.update_yaxes(title_text="density [scaled]", row=i, col=1)
-        fig.update_layout(legend_tracegroupgap=0)
+            sns.lineplot(data=group, x="log2fpkm", y="density", label=source_type, ax=axis)
 
-    # for i, (name, group) in enumerate(mega_df.groupby("sample_name"), start=1):
-    #     fig.add_trace(
-    #         trace=go.Scatter(x=group["log2fpkm"], y=group["density"], mode="lines", name=name, legendgroup=name),
-    #         row=i,
-    #         col=1,
-    #     )
-    #     fig.update_xaxes(title_text="log2(FPKM)", range=[plot_xfloor, max(group["log2fpkm"].tolist())], row=i, col=1)
-    #     fig.update_yaxes(title_text="density [scaled]", row=i, col=1)
-    #     fig.update_layout(legend_tracegroupgap=0)
+        axis.set_xlim(plot_xfloor, sample_data["log2fpkm"].max())
+        axis.set_xlabel("log2(FPKM)")
+        axis.set_ylabel("density [scaled]")
+        axis.legend(title="Source")
 
-    fig.update_layout(height=600 * len(results), width=1000, title_text="zFPKM Plots", showlegend=True)
-    fig.write_image("zfpkm_plot.png")
+    plt.tight_layout()
+    if output_png_filepath.suffix != ".png":
+        logger.warning(
+            f"Output filepath did not end in '.png', setting to '.png' now. Got: '{output_png_filepath.suffix}'"
+        )
+        output_png_filepath = output_png_filepath.with_suffix(".png")
+    plt.savefig(output_png_filepath)
 
 
 def calculate_z_score(metrics: NamedMetrics) -> NamedMetrics:
@@ -602,6 +587,7 @@ def zfpkm_filter(
     force_zfpkm_plot: bool,
     peak_parameters: PeakIdentificationParameters,
     bandwidth: int,
+    output_png_filepath: Path | None,
 ) -> NamedMetrics:
     """Apply zFPKM filtering to the FPKM matrix for a given sample."""
     min_sample_expression = filtering_options.replicate_ratio
@@ -625,7 +611,9 @@ def zfpkm_filter(
                 "If you would like to plot them anyway, set 'force_zfpkm_plot' to True"
             )
         else:
-            zfpkm_plot(results)
+            if output_png_filepath is None:
+                _log_and_raise_error("Output zFPKM PNG filepath is None.", error=ValueError, level=LogLevel.ERROR)
+            zfpkm_plot(results, output_png_filepath=output_png_filepath)
 
         # determine which genes are expressed
         min_samples = round(min_sample_expression * len(zfpkm_df.columns))
@@ -652,6 +640,7 @@ def filter_counts(
     force_zfpkm_plot: bool,
     peak_parameters: PeakIdentificationParameters,
     bandwidth: int,
+    output_png_filepath: Path | None = None,
 ) -> NamedMetrics:
     """Filter the count matrix based on the specified technique."""
     match technique:
@@ -669,6 +658,7 @@ def filter_counts(
                 force_zfpkm_plot=force_zfpkm_plot,
                 peak_parameters=peak_parameters,
                 bandwidth=bandwidth,
+                output_png_filepath=output_png_filepath,
             )
         case FilteringTechnique.UMI:
             # UMI filtering is the same as zFPKM filtering without calculating FPKM
@@ -679,6 +669,7 @@ def filter_counts(
                 force_zfpkm_plot=force_zfpkm_plot,
                 peak_parameters=peak_parameters,
                 bandwidth=bandwidth,
+                output_png_filepath=output_png_filepath,
             )
         case _:
             _log_and_raise_error(
@@ -702,10 +693,11 @@ async def _process(
     technique: FilteringTechnique,
     cut_off: int | float,
     force_zfpkm_plot: bool,
-    output_boolean_activity_filepath: Path,
-    output_zscore_normalization_filepath: Path,
     peak_parameters: PeakIdentificationParameters,
     bandwidth: int,
+    output_boolean_activity_filepath: Path,
+    output_zscore_normalization_filepath: Path,
+    output_zfpkm_png_filepath: Path | None,
 ):
     """Save the results of the RNA-Seq tests to a CSV file."""
     output_boolean_activity_filepath.parent.mkdir(parents=True, exist_ok=True)
@@ -745,6 +737,7 @@ async def _process(
         force_zfpkm_plot=force_zfpkm_plot,
         peak_parameters=peak_parameters,
         bandwidth=bandwidth,
+        output_png_filepath=output_zfpkm_png_filepath,
     )
 
     merged_zscore_df = pd.DataFrame()
@@ -827,6 +820,7 @@ async def rnaseq_gen(
     force_zfpkm_plot: bool = False,
     log_level: LogLevel = LogLevel.INFO,
     log_location: str | TextIOWrapper = sys.stderr,
+    output_zfpkm_png_filepath: Path | None = None,
 ) -> None:
     """Generate a list of active and high-confidence genes from a gene count matrix.
 
@@ -857,6 +851,7 @@ async def rnaseq_gen(
     :param force_zfpkm_plot: If too many samples exist, should plotting be done anyway?
     :param log_level: The level of logging to output
     :param log_location: The location to write logs to
+    :param output_zfpkm_png_filepath: Optional filepath to save zFPKM plots
     :return: None
     """
     _set_up_logging(level=log_level, location=log_location)
@@ -948,8 +943,9 @@ async def rnaseq_gen(
         technique=technique,
         cut_off=cutoff,
         force_zfpkm_plot=force_zfpkm_plot,
-        output_boolean_activity_filepath=output_boolean_activity_filepath,
-        output_zscore_normalization_filepath=output_zscore_normalization_filepath,
         peak_parameters=PeakIdentificationParameters(height=zfpkm_peak_height, distance=zfpkm_peak_distance),
         bandwidth=zfpkm_bandwidth,
+        output_boolean_activity_filepath=output_boolean_activity_filepath,
+        output_zscore_normalization_filepath=output_zscore_normalization_filepath,
+        output_zfpkm_png_filepath=output_zfpkm_png_filepath,
     )

From b846efdd38e77a9791f7c3f428571afbbca84aeb Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 16:16:35 -0600
Subject: [PATCH 254/345] feat: do not throw error if missing png path

---
 main/como/rnaseq_gen.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 6a570c26..2bb3a93b 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -411,7 +411,7 @@ def zfpkm_transform(
     return results, zfpkm_df
 
 
-def zfpkm_plot(results, *, output_png_filepath: Path, plot_xfloor: int = -4, subplot_titles: bool = True):
+def zfpkm_plot(results, *, output_png_filepath: Path, plot_xfloor: int = -4):
     """Plot the log2(FPKM) density and fitted Gaussian for each sample.
 
     :param results: A dictionary of intermediate results from zfpkm_transform.
@@ -612,8 +612,11 @@ def zfpkm_filter(
             )
         else:
             if output_png_filepath is None:
-                _log_and_raise_error("Output zFPKM PNG filepath is None.", error=ValueError, level=LogLevel.ERROR)
-            zfpkm_plot(results, output_png_filepath=output_png_filepath)
+                logger.critical("Output zFPKM PNG filepath is None, set a path to plot zFPKM graphs")
+            else:
+                output_png_filepath.parent.mkdir(parents=True, exist_ok=True)
+                output_png_filepath.unlink(missing_ok=True)
+                zfpkm_plot(results, output_png_filepath=output_png_filepath)
 
         # determine which genes are expressed
         min_samples = round(min_sample_expression * len(zfpkm_df.columns))

From a43485fde22650f18b7f5c94350536de93be7ad1 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 16:29:26 -0600
Subject: [PATCH 255/345] refactor: move NamedTuple to data_types

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/create_context_specific_model.py | 22 ++--------------------
 main/como/data_types.py                    | 17 +++++++++++++++++
 2 files changed, 19 insertions(+), 20 deletions(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 3d123c40..3d2b759c 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -6,7 +6,6 @@
 from collections.abc import Sequence
 from io import TextIOWrapper
 from pathlib import Path
-from typing import NamedTuple
 
 import cobra
 import numpy as np
@@ -20,26 +19,10 @@
 from troppo.methods.reconstruction.imat import IMAT, IMATProperties
 from troppo.methods.reconstruction.tINIT import tINIT, tINITProperties
 
-from como.data_types import Algorithm, CobraCompartments, LogLevel, Solver
+from como.data_types import Algorithm, CobraCompartments, LogLevel, Solver, _BoundaryReactions, _BuildResults
 from como.utils import _log_and_raise_error, _read_file, _set_up_logging, split_gene_expression_data
 
 
-class _BoundaryReactions(NamedTuple):
-    """Boundary reactions to be used in the context specific model."""
-
-    reactions: list[str]
-    lower_bounds: list[float]
-    upper_bounds: list[float]
-
-
-class _BuildResults(NamedTuple):
-    """Results of building a context specific model."""
-
-    model: cobra.Model
-    expression_index_list: list[int]
-    infeasible_reactions: pd.DataFrame
-
-
 def _correct_bracket(rule: str, name: str) -> str:
     """Correct GPR rules to format readable by."""
     rule_match = re.search(r"or|and", rule)
@@ -492,12 +475,11 @@ async def _build_model(  # noqa: C901
             pd.DataFrame({"infeasible_reactions": inconsistent_reactions}),
             pd.DataFrame({"expressed_infeasible_reactions": infeasible_expression_reactions}),
             pd.DataFrame({"infeasible_force_reactions": infeasible_force_reactions}),
-            pd.DataFrame({"infeasible_context_reactions": []}),
+            pd.DataFrame({"infeasible_context_reactions": []}),  # Included to maintain legacy support
         ],
         ignore_index=True,
         axis=0,
     )
-    print(inconsistent_and_infeasible_reactions)
 
     return _BuildResults(
         model=context_model_cobra,
diff --git a/main/como/data_types.py b/main/como/data_types.py
index e4e7d8b2..819f35ba 100644
--- a/main/como/data_types.py
+++ b/main/como/data_types.py
@@ -6,6 +6,7 @@
 from pathlib import Path
 from typing import ClassVar, NamedTuple
 
+import cobra
 import pandas as pd
 from loguru import logger
 
@@ -153,6 +154,22 @@ def get_longhand(cls, shorthand: str) -> str | None:
         return longhand[0] if longhand else None
 
 
+class _BuildResults(NamedTuple):
+    """Results of building a context specific model."""
+
+    model: cobra.Model
+    expression_index_list: list[int]
+    infeasible_reactions: pd.DataFrame
+
+
+class _BoundaryReactions(NamedTuple):
+    """Boundary reactions to be used in the context specific model."""
+
+    reactions: list[str]
+    lower_bounds: list[float]
+    upper_bounds: list[float]
+
+
 @dataclass
 class _BatchEntry:
     batch_num: int

From 0264c3c4c38b3f48f4f6957a73263cc4d71ecab0 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 16:31:21 -0600
Subject: [PATCH 256/345] fix: comment unused variables

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/combine_distributions.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 0096336d..f7bd19d3 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -121,7 +121,7 @@ async def _combine_z_distribution_for_source(
     weighted_matrix = numerator / denominator
     weighted_matrix = np.clip(weighted_matrix, weighted_z_floor, weighted_z_ceiling)
     logger.trace("Finished combining z-distribution")
-    merge_df = pd.concat([merged_source_data, pd.Series(weighted_matrix, name="combined")], axis=1)
+    # merge_df = pd.concat([merged_source_data, pd.Series(weighted_matrix, name="combined")], axis=1)
     weighted_matrix = pd.DataFrame(
         {
             "ensembl_gene_id": merged_source_data["ensembl_gene_id"],
@@ -129,13 +129,13 @@ async def _combine_z_distribution_for_source(
         }
     )
 
-    stack_df = pd.melt(
-        merge_df,
-        id_vars=["ensembl_gene_id"],
-        value_vars=merge_df.columns[1:],  # all other columns are values
-        var_name="source",
-        value_name="zscore",
-    )
+    # stack_df = pd.melt(
+    #     merge_df,
+    #     id_vars=["ensembl_gene_id"],
+    #     value_vars=merge_df.columns[1:],  # all other columns are values
+    #     var_name="source",
+    #     value_name="zscore",
+    # )
     # graph_zscore_distribution(
     #     df=stack_df,
     #     title=f"Combined Z-score Distribution for {context_name}",

From be4e89e22babe8362baa14e9e797335fd557dff0 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 16:32:03 -0600
Subject: [PATCH 257/345] fix: comment unused variables

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/create_context_specific_model.py | 2 +-
 main/como/rnaseq_gen.py                    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 3d2b759c..754cfc9a 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -313,7 +313,7 @@ async def _map_expression_to_reaction(
             error_count += 1
 
     logger.debug(f"Mapped gene expression to reactions, found {error_count} error(s).")
-    expr_vector = np.array(list(reaction_expression.values()), dtype=float)
+    # expr_vector = np.array(list(reaction_expression.values()), dtype=float)
 
     return reaction_expression
 
diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 2bb3a93b..13181f30 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -802,7 +802,7 @@ async def _process(
     logger.success(f"Wrote boolean matrix to {output_boolean_activity_filepath}")
 
 
-async def rnaseq_gen(
+async def rnaseq_gen(  # noqa: C901
     context_name: str,
     input_rnaseq_filepath: Path,
     input_gene_info_filepath: Path,

From ab96976ef2bb8f125f4f21175a638187d2ae6940 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 16:32:36 -0600
Subject: [PATCH 258/345] fix: line too long

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 0846d6d2..013d7f49 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -357,7 +357,8 @@ async def _create_config_df(  # noqa: C901
             _log_and_raise_error(
                 (
                     f"\n\nFilename of '{gene_count_filename}' is not valid. "
-                    f"Should be 'contextName_SXRYrZ.tab', where X is the study/batch number, Y is the replicate number, "
+                    f"Should be 'contextName_SXRYrZ.tab', "
+                    f"where X is the study/batch number, Y is the replicate number, "
                     f"and Z is the run number."
                     "\n\nIf not a multi-run sample, exclude 'rZ' from the filename."
                 ),

From 8a0f4c6cfb211e6526119cfacda2ad8bd53e3e5c Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 16:43:42 -0600
Subject: [PATCH 259/345] style: ruff formatting and linting fixes

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/COMO.ipynb             |  5 ++---
 main/como/__init__.py       |  3 +--
 main/como/graph/__init__.py | 20 +++++++++++++-------
 ruff.toml                   |  2 +-
 4 files changed, 17 insertions(+), 13 deletions(-)

diff --git a/main/COMO.ipynb b/main/COMO.ipynb
index 8d79c4be..82647f6e 100644
--- a/main/COMO.ipynb
+++ b/main/COMO.ipynb
@@ -242,8 +242,8 @@
    "source": [
     "from pathlib import Path\n",
     "\n",
-    "from como.rnaseq_preprocess import rnaseq_preprocess\n",
     "from como.data_types import RNAType\n",
+    "from como.rnaseq_preprocess import rnaseq_preprocess\n",
     "\n",
     "taxon_id = 9606\n",
     "context_names = [\"naiveB\"]\n",
@@ -738,7 +738,7 @@
     }
    },
    "source": [
-    "from como.merge_xomics import merge_xomics, AdjustmentMethod\n",
+    "from como.merge_xomics import AdjustmentMethod, merge_xomics\n",
     "\n",
     "total_rna_weight = 6\n",
     "mrna_weight = 6\n",
@@ -889,7 +889,6 @@
    "outputs": [],
    "source": [
     "import json\n",
-    "import os\n",
     "from pathlib import Path\n",
     "\n",
     "from como.project import Config\n",
diff --git a/main/como/__init__.py b/main/como/__init__.py
index 660fc21f..c84ef56e 100644
--- a/main/como/__init__.py
+++ b/main/como/__init__.py
@@ -1,6 +1,5 @@
+from como.data_types import AdjustmentMethod, Algorithm, CobraCompartments, FilteringTechnique, LogLevel, Solver
 from como.utils import stringlist_to_list
-from como.data_types import LogLevel, FilteringTechnique, AdjustmentMethod, Solver, Algorithm, CobraCompartments
-
 
 __all__ = [
     "AdjustmentMethod",
diff --git a/main/como/graph/__init__.py b/main/como/graph/__init__.py
index 1f6c6a22..7594db2f 100644
--- a/main/como/graph/__init__.py
+++ b/main/como/graph/__init__.py
@@ -1,11 +1,9 @@
 from pathlib import Path
-from loguru import logger
+
 import pandas as pd
-from matplotlib import pyplot as plt
 import seaborn as sns
-
-
-__all__ = ["z_score_distribution"]
+from loguru import logger
+from matplotlib import pyplot as plt
 
 
 def z_score_distribution(
@@ -13,12 +11,20 @@ def z_score_distribution(
     title: str,
     output_filepath: Path,
 ):
+    """Graph a z-score distribution.
+
+    :param df: The z-score data to graph
+    :param title: Title to add to graph
+    :param output_filepath: Output PNG filepath location
+    :return: None
+    """
     if output_filepath.suffix not in {".png", ".pdf", ".svg"}:
         logger.warning(
-            f"Expected .png, .pdf, or .svg suffix for output_png_filepath, got {output_filepath.suffix}. Defaulting to .pdf"
+            f"Expected .png, .pdf, or .svg suffix for output_png_filepath, got {output_filepath.suffix}. "
+            f"Defaulting to .pdf"
         )
         output_filepath = output_filepath.with_suffix(".pdf")
-    logger.trace(f"Graphing z-score distribution")
+    logger.trace("Graphing z-score distribution")
     output_filepath.parent.mkdir(parents=True, exist_ok=True)
     output_filepath.unlink(missing_ok=True)
 
diff --git a/ruff.toml b/ruff.toml
index 3691008f..fe0bd163 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -1,6 +1,5 @@
 line-length = 120
 extend-include = ["docs/**/*.py", "tests/**/*.py", "**/*.ipynb"]
-exclude = ["__init__.py"]
 
 [format]
 quote-style = "double"
@@ -46,6 +45,7 @@ ignore = [
     "D101",     # allow undocumented public class
     "D102",     # allow undocumented class method
     "D103",     # allow undocumented public method definitions
+    "D104",     # allow undocumented public package
     "F811",     # allow redefinition of variables, required for pytest fixtures
     "S101",     # allow use of `assert` in test files
 ]

From 7e53dac311402a3f0537c03bcd408e6cff9053cc Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 16:45:46 -0600
Subject: [PATCH 260/345] fix: allow undocumented public package

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 ruff.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/ruff.toml b/ruff.toml
index fe0bd163..eeea6f84 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -34,6 +34,7 @@ select = [
 ignore = [
     "D100",     # allow undocumented public module definitions
     "D101",     # allow undocumented public class
+    "D104",     # allow undocumented public package
     "D203",     # do not require one blank line before class docstring
     "D213",     # first docstring line should be on the second line
     "TRY003",   # allow exception messages outside the `Exception` class

From bd65dbe64a2374396b4b7f432284d76fac870947 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 16:49:29 -0600
Subject: [PATCH 261/345] fix: do not create virtual environment to format
 notebooks

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 .github/workflows/continuous_integration.yml | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/.github/workflows/continuous_integration.yml b/.github/workflows/continuous_integration.yml
index 113c905d..43634ea0 100644
--- a/.github/workflows/continuous_integration.yml
+++ b/.github/workflows/continuous_integration.yml
@@ -16,12 +16,6 @@ jobs:
       - name: Install uv
         uses: astral-sh/setup-uv@v4
 
-      - name: Create Virtual Environment
-        run: uv venv
-
-      - name: Install Jupyter
-        run: uv pip install jupyter nbconvert
-
       - name: Strip Notebook Output
         run: uv run jupyter nbconvert --clear-output --inplace "main/COMO.ipynb"
 

From e419816af09ba122a5af38e8cb47ccfafc62299e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 16:51:31 -0600
Subject: [PATCH 262/345] fix: use python 3.10 to evade errors

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 .github/workflows/continuous_integration.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/continuous_integration.yml b/.github/workflows/continuous_integration.yml
index 43634ea0..f7a3426f 100644
--- a/.github/workflows/continuous_integration.yml
+++ b/.github/workflows/continuous_integration.yml
@@ -17,7 +17,7 @@ jobs:
         uses: astral-sh/setup-uv@v4
 
       - name: Strip Notebook Output
-        run: uv run jupyter nbconvert --clear-output --inplace "main/COMO.ipynb"
+        run: uv run --python 3.10 jupyter nbconvert --clear-output --inplace "main/COMO.ipynb"
 
       - name: Format Python Imports
         uses: astral-sh/ruff-action@v2

From 7fa8d0027c554e7dc542abecad24cae56a806e2a Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 16:57:03 -0600
Subject: [PATCH 263/345] fix: use uv tool to run nbconvert

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 .github/workflows/continuous_integration.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/continuous_integration.yml b/.github/workflows/continuous_integration.yml
index f7a3426f..956807f7 100644
--- a/.github/workflows/continuous_integration.yml
+++ b/.github/workflows/continuous_integration.yml
@@ -17,7 +17,7 @@ jobs:
         uses: astral-sh/setup-uv@v4
 
       - name: Strip Notebook Output
-        run: uv run --python 3.10 jupyter nbconvert --clear-output --inplace "main/COMO.ipynb"
+        run: uv tool run --from nbconvert jupyter-nbconvert --clear-output --inplace "main/COMO.ipynb"
 
       - name: Format Python Imports
         uses: astral-sh/ruff-action@v2

From c6f02bc9d4cbf34de1e71146d898ecf346f509cc Mon Sep 17 00:00:00 2001
From: JoshLoecker <47901540+JoshLoecker@users.noreply.github.com>
Date: Sun, 12 Jan 2025 22:57:30 +0000
Subject: [PATCH 264/345] style: format code, Jupyter Notebook(s), and Python
 imports with `ruff`

---
 main/como/create_context_specific_model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 754cfc9a..701c09ef 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -81,7 +81,7 @@ def _gene_rule_logical(gpr_expression: str, level: int = 0) -> str:
         inner_string = inner_string.replace("[", "")
         inner_string = inner_string.replace("]", "")
 
-    expression_out = f"{gpr_expression[:loc_l]}{inner_string}{gpr_expression[loc_r + 1:]}"
+    expression_out = f"{gpr_expression[:loc_l]}{inner_string}{gpr_expression[loc_r + 1 :]}"
     expression_out = _gene_rule_logical(expression_out, level + 1)
 
     return expression_out

From 06461507543ab414e07ddbac26149e0cb1ab2897 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 16:59:36 -0600
Subject: [PATCH 265/345] feat: expand test suite to include python 3.11 and
 3.12

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 .github/workflows/continuous_integration.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/continuous_integration.yml b/.github/workflows/continuous_integration.yml
index 956807f7..9169dd9b 100644
--- a/.github/workflows/continuous_integration.yml
+++ b/.github/workflows/continuous_integration.yml
@@ -56,7 +56,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ "3.10" ]
+        python-version: [ "3.10", "3.11", "3.12" ]
     steps:
       - name: Checkout
         uses: actions/checkout@v4

From 9a72ae5b300c42681d35b4719e3d760c9582c1ed Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 17:01:07 -0600
Subject: [PATCH 266/345] fix: check if provided data is a path before checking
 if it exists

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/utils.py b/main/como/utils.py
index a25c8b3e..db3886b7 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -149,7 +149,7 @@ async def _read_file(
     if not path:
         return None
 
-    if not path.exists():
+    if isinstance(path, Path) and not path.exists():
         logger.critical(f"File {path} does not exist")
         raise FileNotFoundError(f"File {path} does not exist")
 

From 8259105e0fd9386c6c55dfd342dc8db856088a88 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 17:04:40 -0600
Subject: [PATCH 267/345] fix: read StringIO data instead of attempting to make
 dataframe from it

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/utils.py b/main/como/utils.py
index db3886b7..b3e51361 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -155,7 +155,7 @@ async def _read_file(
 
     # StringIO is used if a CSV file is read using open() directly
     if isinstance(path, io.StringIO):
-        return pd.DataFrame(path, **kwargs)
+        return pd.read_csv(path, **kwargs)
 
     match path.suffix:
         case ".csv" | ".tsv":

From 330ed84af5752dd4dd35493723c7bb088e53edc3 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Sun, 12 Jan 2025 17:15:30 -0600
Subject: [PATCH 268/345] fix: statsmodels version dependent on python version

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 pyproject.toml | 11 +++++++++++
 uv.lock        | 40 ++++++++++++++++++++++++++++++++--------
 2 files changed, 43 insertions(+), 8 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index c6921422..ffae336b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -22,6 +22,9 @@ dependencies = [
     "scipy>=1.7.3",
     "seaborn>=0.13.2",
     "setuptools<60.0",
+    # Statsmodels >= 0.14 is required for python 3.12+
+    "statsmodels>=0.13.0",
+    "statsmodels>=0.14.0; python_version >= '3.12'",
     "troppo@git+https://github.com/JoshLoecker/troppo@master",
 ]
 
@@ -60,3 +63,11 @@ tag_format = "$version"
 version_scheme = "semver2"
 version_provider = "pep621"
 update_changelog_on_bump = true
+
+[tool.uv.sources]
+como = { workspace = true }
+
+[dependency-groups]
+dev = [
+    "como",
+]
diff --git a/uv.lock b/uv.lock
index 81292416..0a32e30b 100644
--- a/uv.lock
+++ b/uv.lock
@@ -442,6 +442,7 @@ dependencies = [
     { name = "scipy" },
     { name = "seaborn" },
     { name = "setuptools" },
+    { name = "statsmodels" },
     { name = "troppo" },
 ]
 
@@ -463,6 +464,11 @@ interactive = [
     { name = "jupyterlab" },
 ]
 
+[package.dev-dependencies]
+dev = [
+    { name = "como" },
+]
+
 [package.metadata]
 requires-dist = [
     { name = "aiofiles", specifier = ">=24.1.0" },
@@ -490,9 +496,14 @@ requires-dist = [
     { name = "scipy", specifier = ">=1.7.3" },
     { name = "seaborn", specifier = ">=0.13.2" },
     { name = "setuptools", specifier = "<60.0" },
+    { name = "statsmodels", specifier = ">=0.13.0" },
+    { name = "statsmodels", marker = "python_full_version >= '3.12'", specifier = ">=0.14.0" },
     { name = "troppo", git = "https://github.com/JoshLoecker/troppo?rev=master" },
 ]
 
+[package.metadata.requires-dev]
+dev = [{ name = "como", editable = "." }]
+
 [[package]]
 name = "contourpy"
 version = "1.2.1"
@@ -2640,7 +2651,7 @@ wheels = [
 
 [[package]]
 name = "statsmodels"
-version = "0.13.2"
+version = "0.14.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
@@ -2649,13 +2660,26 @@ dependencies = [
     { name = "patsy" },
     { name = "scipy" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/e1/4a/0eb4efa49cc352e2721e2aebfe8573264db2add195545ec3979c98040c3b/statsmodels-0.13.2.tar.gz", hash = "sha256:77dc292c9939c036a476f1770f9d08976b05437daa229928da73231147cde7d4", size = 17874260 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/7b/88/684887d7c57e1d51fcf6dc5b73f7eb1fde22602a9b588784e051760abb3f/statsmodels-0.13.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:3e7ca5b7e678c0bb7a24f5c735d58ac104a50eb61b17c484cce0e221a095560f", size = 9678336 },
-    { url = "https://files.pythonhosted.org/packages/9a/d7/a58f9edc176f13da02e2a3b2467069f9f8a3ba960a72cbf4b228d3e378bd/statsmodels-0.13.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:066a75d5585378b2df972f81a90b9a3da5e567b7d4833300c1597438c1a35e29", size = 9134246 },
-    { url = "https://files.pythonhosted.org/packages/fe/4c/150b6f9d1699f94c79df1dea6888e991c2485d22be93cae4a94166ff0efb/statsmodels-0.13.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f15f38dfc9c5c091662cb619e12322047368c67aef449c7554d9b324a15f7a94", size = 9648545 },
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-    { url = "https://files.pythonhosted.org/packages/b8/ad/820c01123f5b3d596965d5e5b274830d38bc39f93b11d76a3b53f86f45f1/statsmodels-0.13.2-cp310-cp310-win_amd64.whl", hash = "sha256:855b1cc2a91ab140b9bcf304b1731705805ce73223bf500b988804968554c0ed", size = 9138751 },
+sdist = { url = "https://files.pythonhosted.org/packages/1f/3b/963a015dd8ea17e10c7b0e2f14d7c4daec903baf60a017e756b57953a4bf/statsmodels-0.14.4.tar.gz", hash = "sha256:5d69e0f39060dc72c067f9bb6e8033b6dccdb0bae101d76a7ef0bcc94e898b67", size = 20354802 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/af/2c/23bf5ad9e8a77c0c8d9750512bff89e32154dea91998114118e0e147ae67/statsmodels-0.14.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:7a62f1fc9086e4b7ee789a6f66b3c0fc82dd8de1edda1522d30901a0aa45e42b", size = 10216574 },
+    { url = "https://files.pythonhosted.org/packages/ba/a5/2f09ab918296e534ea5d132e90efac51ae12ff15992d77539bbfca1158fa/statsmodels-0.14.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:46ac7ddefac0c9b7b607eed1d47d11e26fe92a1bc1f4d9af48aeed4e21e87981", size = 9912430 },
+    { url = "https://files.pythonhosted.org/packages/93/6a/b86f8c9b799dc93e5b4a3267eb809843e6328e34248a53496b96f50d732e/statsmodels-0.14.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2a337b731aa365d09bb0eab6da81446c04fde6c31976b1d8e3d3a911f0f1e07b", size = 10444673 },
+    { url = "https://files.pythonhosted.org/packages/78/44/d72c634211797ed07dd8c63ced4ae11debd7a40b24ee80e79346a526194f/statsmodels-0.14.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:631bb52159117c5da42ba94bd94859276b68cab25dc4cac86475bc24671143bc", size = 10811248 },
+    { url = "https://files.pythonhosted.org/packages/35/64/df81426924fcc48a0402534efa96cde13275629ae52f123189d16c4b75ff/statsmodels-0.14.4-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:3bb2e580d382545a65f298589809af29daeb15f9da2eb252af8f79693e618abc", size = 10946447 },
+    { url = "https://files.pythonhosted.org/packages/5c/f9/205130cceeda0eebd5a1a58c04e060c2f87a1d63cbbe37a9caa0fcb50c68/statsmodels-0.14.4-cp310-cp310-win_amd64.whl", hash = "sha256:9729642884147ee9db67b5a06a355890663d21f76ed608a56ac2ad98b94d201a", size = 9845796 },
+    { url = "https://files.pythonhosted.org/packages/48/88/326f5f689e69d9c47a68a22ffdd20a6ea6410b53918f9a8e63380dfc181c/statsmodels-0.14.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:5ed7e118e6e3e02d6723a079b8c97eaadeed943fa1f7f619f7148dfc7862670f", size = 10221032 },
+    { url = "https://files.pythonhosted.org/packages/07/0b/9a0818be42f6689ebdc7a2277ea984d6299f0809d0e0277128df4f7dc606/statsmodels-0.14.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f5f537f7d000de4a1708c63400755152b862cd4926bb81a86568e347c19c364b", size = 9912219 },
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+    { url = "https://files.pythonhosted.org/packages/81/0c/2453eec3ac25e300847d9ed97f41156de145e507391ecb5ac989e111e525/statsmodels-0.14.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:bb695c2025d122a101c2aca66d2b78813c321b60d3a7c86bb8ec4467bb53b0f9", size = 10879534 },
+    { url = "https://files.pythonhosted.org/packages/59/9a/e466a1b887a1441141e52dbcc98152f013d85076576da6eed2357f2016ae/statsmodels-0.14.4-cp312-cp312-win_amd64.whl", hash = "sha256:7f7917a51766b4e074da283c507a25048ad29a18e527207883d73535e0dc6184", size = 9823866 },
 ]
 
 [[package]]

From b6b663313041ad7ae9633fcb055eb573f18100f6 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 13 Jan 2025 09:42:01 -0600
Subject: [PATCH 269/345] fix: use tpm instead of quantile

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/data_types.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/data_types.py b/main/como/data_types.py
index 819f35ba..54d19b55 100644
--- a/main/como/data_types.py
+++ b/main/como/data_types.py
@@ -39,7 +39,7 @@ class FilteringTechnique(Enum):
 
     CPM = "cpm"
     ZFPKM = "zfpkm"
-    TPM = "quantile"
+    TPM = "tpm"
     UMI = "umi"
 
 

From 21f780bcd53a2167ced1144af9aa2f7cea8e0096 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 13 Jan 2025 09:42:18 -0600
Subject: [PATCH 270/345] fix: typo

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/utils.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/main/como/utils.py b/main/como/utils.py
index b3e51361..b28ab3ca 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -123,11 +123,11 @@ async def _format_determination(
     :return: A pandas DataFrame
     """
     requested_output = [requested_output] if isinstance(requested_output, Output) else requested_output
-    cohersion = (await biodbnet.db_find(values=input_values, output_db=requested_output, taxon=taxon)).drop(
+    coercion = (await biodbnet.db_find(values=input_values, output_db=requested_output, taxon=taxon)).drop(
         columns=["Input Type"]
     )
-    cohersion.columns = pd.Index(["input_value", *[o.value.replace(" ", "_").lower() for o in requested_output]])
-    return cohersion
+    coercion.columns = pd.Index(["input_value", *[o.value.replace(" ", "_").lower() for o in requested_output]])
+    return coercion
 
 
 async def _read_file(

From b119787ca531e1f4c23147fd3f747f8128ae35fb Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 13 Jan 2025 09:42:41 -0600
Subject: [PATCH 271/345] chore: update uv lock

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 uv.lock | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/uv.lock b/uv.lock
index 0a32e30b..627dcf41 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,9 +1,9 @@
 version = 1
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
-    "python_full_version < '3.11'",
-    "python_full_version == '3.11.*'",
     "python_full_version >= '3.12'",
+    "python_full_version == '3.11.*'",
+    "python_full_version < '3.11'",
 ]
 
 [[package]]

From a441231f29f22d34870ec7e9482b2dea548467f0 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 13 Jan 2025 09:44:01 -0600
Subject: [PATCH 272/345] chore: code re-arrangement

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/data_types.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/main/como/data_types.py b/main/como/data_types.py
index 54d19b55..9b633999 100644
--- a/main/como/data_types.py
+++ b/main/como/data_types.py
@@ -59,11 +59,6 @@ class LogLevel(Enum):
     CRITICAL = 50
 
 
-class PeakIdentificationParameters(NamedTuple):
-    height: float
-    distance: float
-
-
 class RNAType(Enum):
     TRNA = "total"
     MRNA = "mrna"
@@ -86,6 +81,11 @@ class SourceTypes(Enum):
     PROTEOMICS = "proteomics"
 
 
+class PeakIdentificationParameters(NamedTuple):
+    height: float
+    distance: float
+
+
 class CobraCompartments:
     """Convert from compartment "long-hand" to "short-hand".
 

From 09e1852e385024ece32860d644168d178ed33358 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 13 Jan 2025 09:57:39 -0600
Subject: [PATCH 273/345] chore: remove aiofiles as a dependency

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 pyproject.toml | 2 --
 uv.lock        | 2 --
 2 files changed, 4 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index ffae336b..af0963d7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,7 +7,6 @@ name = "COMO"
 dynamic = ["version"]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "aiofiles>=24.1.0",
     "aioftp>=0.23.1",
     "cobamp@git+https://github.com/JoshLoecker/cobamp@master",
     "cobra>=0.28.0",
@@ -22,7 +21,6 @@ dependencies = [
     "scipy>=1.7.3",
     "seaborn>=0.13.2",
     "setuptools<60.0",
-    # Statsmodels >= 0.14 is required for python 3.12+
     "statsmodels>=0.13.0",
     "statsmodels>=0.14.0; python_version >= '3.12'",
     "troppo@git+https://github.com/JoshLoecker/troppo@master",
diff --git a/uv.lock b/uv.lock
index 627dcf41..1a227653 100644
--- a/uv.lock
+++ b/uv.lock
@@ -427,7 +427,6 @@ name = "como"
 version = "1.11.1"
 source = { editable = "." }
 dependencies = [
-    { name = "aiofiles" },
     { name = "aioftp" },
     { name = "cobamp" },
     { name = "cobra" },
@@ -471,7 +470,6 @@ dev = [
 
 [package.metadata]
 requires-dist = [
-    { name = "aiofiles", specifier = ">=24.1.0" },
     { name = "aioftp", specifier = ">=0.23.1" },
     { name = "cobamp", git = "https://github.com/JoshLoecker/cobamp?rev=master" },
     { name = "cobra", specifier = ">=0.28.0" },

From 80f32d425397fa2b68d6f25b9b09d57a4e5bd2a3 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 14 Jan 2025 14:17:10 -0600
Subject: [PATCH 274/345] fix: renamed RNAType.(trna,mrna) to
 RNAType.(TRNA,MRNA)

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 013d7f49..2a7b2bd5 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -602,9 +602,9 @@ async def _process(
 ):
     rna_types: list[tuple[RNAType, Path, Path]] = []
     if output_trna_config_filepath:
-        rna_types.append((RNAType.trna, output_trna_config_filepath, output_trna_matrix_filepath))
+        rna_types.append((RNAType.TRNA, output_trna_config_filepath, output_trna_matrix_filepath))
     if output_mrna_config_filepath:
-        rna_types.append((RNAType.mrna, output_mrna_config_filepath, output_mrna_matrix_filepath))
+        rna_types.append((RNAType.MRNA, output_mrna_config_filepath, output_mrna_matrix_filepath))
 
     # if provided, iterate through como-input specific directories
     tasks = []

From 2c1687b36cb637b2c9ce6129e14e213634c93d47 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 14 Jan 2025 14:18:14 -0600
Subject: [PATCH 275/345] fix: re-attempt processing if JSON decode error
 occurs

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 2a7b2bd5..344a07b5 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import asyncio
+import json
 import re
 import sys
 from dataclasses import dataclass, field
@@ -521,11 +522,16 @@ async def _create_gene_info_file(
 
     async def read_counts(file: Path) -> list[str]:
         data = await _read_file(file, h5ad_as_df=False)
-        conversion = await (
-            ensembl_to_gene_id_and_symbol(ids=data["ensembl_gene_id"].tolist(), taxon=taxon)
-            if isinstance(data, pd.DataFrame)
-            else gene_symbol_to_ensembl_and_gene_id(symbols=data.var_names.tolist(), taxon=taxon)
-        )
+
+        try:
+            conversion = await (
+                ensembl_to_gene_id_and_symbol(ids=data["ensembl_gene_id"].tolist(), taxon=taxon)
+                if isinstance(data, pd.DataFrame)
+                else gene_symbol_to_ensembl_and_gene_id(symbols=data.var_names.tolist(), taxon=taxon)
+            )
+        except json.JSONDecodeError:
+            logger.warning(f"Got a JSON decode error for file '{counts_matrix_filepaths}', exiting")
+            exit()
 
         # Remove NA values from entrez_gene_id dataframe column
         return conversion["entrez_gene_id"].dropna().tolist()

From 0f317f31e76b0e20dd4ecf32614c00fe72566e5b Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 14 Jan 2025 14:18:39 -0600
Subject: [PATCH 276/345] refactor: remove dependency on aiofiles

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 344a07b5..943476b9 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -10,7 +10,6 @@
 from pathlib import Path
 from typing import Literal
 
-import aiofiles
 import numpy as np
 import pandas as pd
 from fast_bioservices.biothings.mygene import MyGene
@@ -43,17 +42,21 @@ async def build_from_tab(cls, filepath: Path) -> _STARinformation:
                 level=LogLevel.ERROR,
             )
 
-        async with aiofiles.open(filepath) as i_stream:
-            unmapped = await i_stream.readline()
-            multimapping = await i_stream.readline()
-            no_feature = await i_stream.readline()
-            ambiguous = await i_stream.readline()
+        with filepath.open("r") as i_stream:
+            unmapped, multimapping, no_feature, ambiguous = await asyncio.gather(
+                *[
+                    asyncio.to_thread(i_stream.readline),
+                    asyncio.to_thread(i_stream.readline),
+                    asyncio.to_thread(i_stream.readline),
+                    asyncio.to_thread(i_stream.readline),
+                ]
+            )
 
             num_unmapped = [int(i) for i in unmapped.rstrip("\n").split("\t")[1:]]
             num_multimapping = [int(i) for i in multimapping.rstrip("\n").split("\t")[1:]]
             num_no_feature = [int(i) for i in no_feature.rstrip("\n").split("\t")[1:]]
             num_ambiguous = [int(i) for i in ambiguous.rstrip("\n").split("\t")[1:]]
-            remainder = await i_stream.read()
+            remainder = await asyncio.to_thread(i_stream.read)
 
         df = await _read_file(StringIO(remainder), sep="\t", header=None)
         df.columns = [

From 2229c51d64f52b8bbe49420269c75d4a6be33a9e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 14 Jan 2025 14:18:51 -0600
Subject: [PATCH 277/345] fix: do not call listify twice

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 943476b9..12a5b82a 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -702,7 +702,6 @@ async def rnaseq_preprocess(
         else output_mrna_count_matrix_filepath
     )
 
-    input_matrix_filepath = _listify(input_matrix_filepath)
     await _process(
         context_name=context_name,
         taxon=taxon,

From 9a6a42dc51987141883b91e808b6890ec072c958 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 14 Jan 2025 14:19:14 -0600
Subject: [PATCH 278/345] refactor: remove dependency on aiofiles

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/combine_distributions.py | 17 ++++-------------
 1 file changed, 4 insertions(+), 13 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index f7bd19d3..3571f122 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -3,7 +3,6 @@
 import asyncio
 from pathlib import Path
 
-import aiofiles.os
 import numpy as np
 import pandas as pd
 from loguru import logger
@@ -34,12 +33,8 @@ async def _combine_z_distribution_for_batch(
     weighted_z_floor: int,
     weighted_z_ceiling: int,
 ) -> pd.DataFrame:
-    await asyncio.gather(
-        *[
-            aiofiles.os.makedirs(output_combined_matrix_filepath.parent.as_posix(), exist_ok=True),
-            aiofiles.os.makedirs(output_figure_dirpath.as_posix(), exist_ok=True),
-        ]
-    )
+    output_combined_matrix_filepath.parent.mkdir(parents=True, exist_ok=True)
+    output_figure_dirpath.mkdir(parents=True, exist_ok=True)
 
     logger.trace(
         f"Combining z-score distributions: batch #{batch.batch_num}, "
@@ -102,12 +97,8 @@ async def _combine_z_distribution_for_source(
         merged_source_data.columns = ["ensembl_gene_id", "combine_z"]
         return merged_source_data
 
-    await asyncio.gather(
-        *[
-            aiofiles.os.makedirs(output_combined_matrix_filepath.parent.as_posix(), exist_ok=True),
-            aiofiles.os.makedirs(output_figure_filepath.parent.as_posix(), exist_ok=True),
-        ]
-    )
+    output_combined_matrix_filepath.parent.mkdir(parents=True, exist_ok=True)
+    output_figure_filepath.parent.mkdir(parents=True, exist_ok=True)
 
     logger.trace(f"Found {_num_columns(merged_source_data) - 1} samples for context '{context_name}' to combine")
     values = merged_source_data.iloc[:, 1:].values

From f288015d7c1a4294fff50f3aa29906df85ec76e9 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 14 Jan 2025 14:20:03 -0600
Subject: [PATCH 279/345] refactor: allow providing a list of model filepaths
 to write to

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/create_context_specific_model.py | 58 +++++++++++++---------
 1 file changed, 34 insertions(+), 24 deletions(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 701c09ef..0e408be0 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -547,20 +547,21 @@ async def _collect_boundary_reactions(path: Path) -> _BoundaryReactions:
     )
 
 
-async def _write_model_to_disk(
-    model: cobra.Model,
-    output_directory: Path,
-    context_name: str,
-    output_filetypes: list[str],
-    algorithm: Algorithm,
-) -> None:
-    output_directory.mkdir(parents=True, exist_ok=True)
-    if "mat" in output_filetypes:
-        cobra.io.save_matlab_model(model, (output_directory / f"{context_name}_SpecificModel_{algorithm.value}.mat"))
-    if "json" in output_filetypes:
-        cobra.io.save_json_model(model, (output_directory / f"{context_name}_SpecificModel_{algorithm.value}.json"))
-    if "xml" in output_filetypes:
-        cobra.io.write_sbml_model(model, (output_directory / f"{context_name}_SpecificModel_{algorithm.value}.xml"))
+def _write_model_to_disk(context_name: str, model: cobra.Model, output_filepaths: list[Path]) -> None:
+    for path in output_filepaths:
+        if path.suffix == ".mat":
+            cobra.io.save_matlab_model(model=model, file_name=path)
+        elif path.suffix == ".json":
+            cobra.io.save_json_model(model=model, filename=path, pretty=True)
+        elif path.suffix in {".sbml", ".xml"}:
+            cobra.io.write_sbml_model(cobra_model=model, filename=path)
+        else:
+            _log_and_raise_error(
+                f"Invalid output model filetype. Should be one of .xml, .sbml, .mat, or .json. Got '{path.suffix}'",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
+        logger.success(f"Saved metabolic model for context '{context_name}' to '{path}'")
 
 
 async def create_context_specific_model(  # noqa: C901
@@ -568,8 +569,8 @@ async def create_context_specific_model(  # noqa: C901
     reference_model: Path,
     active_genes_filepath: Path,
     output_infeasible_reactions_filepath: Path,
-    output_model_dirpath: Path,
     output_flux_result_filepath: Path,
+    output_model_filepaths: Path | list[Path],
     output_filetypes: list[str] | None = None,
     output_fastcore_expression_index_filepath: Path | None = None,
     objective: str = "biomass_reaction",
@@ -585,6 +586,23 @@ async def create_context_specific_model(  # noqa: C901
 ):
     """Create a context-specific model using the provided data."""
     _set_up_logging(level=log_level, location=log_location)
+    output_model_filepaths = (
+        [output_model_filepaths] if isinstance(output_model_filepaths, Path) else output_model_filepaths
+    )
+    for path in output_model_filepaths:
+        if path.suffix not in {".mat", ".xml", ".sbml", ".json"}:
+            _log_and_raise_error(
+                f"Invalid output model filetype. Should be one of .xml, .sbml, .mat, or .json. Got '{path.suffix}'",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
+    if len(output_model_filepaths) != len(output_model_filepaths):
+        _log_and_raise_error(
+            "The number of output model filepaths must be the same as the number of output flux result filepaths",
+            error=ValueError,
+            level=LogLevel.ERROR,
+        )
+
     if not reference_model.exists():
         _log_and_raise_error(
             f"Reference model not found at {reference_model}",
@@ -685,15 +703,7 @@ async def create_context_specific_model(  # noqa: C901
         fastcore_df.dropna(inplace=True)
         fastcore_df.to_csv(output_fastcore_expression_index_filepath, index=False)
 
-    await _write_model_to_disk(
-        model=build_results.model,
-        output_directory=output_model_dirpath,
-        context_name=context_name,
-        output_filetypes=output_filetypes,
-        algorithm=algorithm,
-    )
-
-    logger.success(f"Saved metabolic model for context '{context_name}' to {output_model_dirpath}")
+    _write_model_to_disk(context_name=context_name, model=build_results.model, output_filepaths=output_model_filepaths)
     logger.debug(f"Number of Genes: {len(build_results.model.genes):,}")
     logger.debug(f"Number of Metabolites: {len(build_results.model.metabolites):,}")
     logger.debug(f"Number of Reactions: {len(build_results.model.reactions):,}")

From c0d38168f407aad1d7857ab1f43488bbc29d4111 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 14 Jan 2025 14:20:28 -0600
Subject: [PATCH 280/345] fix: check that license information is present if
 using gurobi

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/create_context_specific_model.py | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 0e408be0..a375c815 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -678,6 +678,28 @@ async def create_context_specific_model(  # noqa: C901
             )
         force_rxns = df["abbreviation"].tolist()
 
+    # Test that gurobi is using a valid license file
+    if solver == Solver.GUROBI:
+        # test if gurobi is available
+        try:
+            import gurobipy as gp
+        except ImportError as e:
+            logger.error(
+                "The gurobi solver requires the gurobipy package to be installed. "
+                "Please install gurobipy and try again. "
+                "This can be done by installing the 'gurobi' optional dependency."
+            )
+            raise ImportError from e
+
+        env = gp.Env()
+        if env.getParam("WLSACCESSID") == "" or env.getParam("WLSSECRET") == "":
+            logger.critical(
+                "You have requested to use the Gurobi solver but license information cannot be found. "
+                "COMO will continue, but it is HIGHLY unlikely the resulting model will be valid."
+            )
+        # remove gurobi-related information, it is no longer required
+        del env, gp
+
     logger.info(f"Creating '{context_name}' model using '{algorithm.value}' reconstruction and '{solver.value}' solver")
     build_results: _BuildResults = await _build_model(
         general_model_file=reference_model,

From df86903095e98193b9f5b24c5e536acea8f7f27f Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 14 Jan 2025 14:20:40 -0600
Subject: [PATCH 281/345] feat: add NONE for no logging

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/data_types.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/main/como/data_types.py b/main/como/data_types.py
index 9b633999..f9c11ca5 100644
--- a/main/como/data_types.py
+++ b/main/como/data_types.py
@@ -57,6 +57,7 @@ class LogLevel(Enum):
     WARNING = 30
     ERROR = 40
     CRITICAL = 50
+    NONE = 100
 
 
 class RNAType(Enum):

From f9250789fa17446711cc9513b476f6d91a3aba8e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 14 Jan 2025 14:22:30 -0600
Subject: [PATCH 282/345] refactor: remove dependency on aiofiles

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/utils.py | 19 ++++---------------
 1 file changed, 4 insertions(+), 15 deletions(-)

diff --git a/main/como/utils.py b/main/como/utils.py
index b28ab3ca..22652f6a 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -3,16 +3,13 @@
 import asyncio
 import contextlib
 import functools
-import inspect
 import io
 import sys
 from collections.abc import Iterator
 from concurrent.futures import ThreadPoolExecutor
 from io import TextIOWrapper
 from pathlib import Path
-from typing import Union
 
-import aiofiles
 import numpy.typing as npt
 import pandas as pd
 import scanpy as sc
@@ -160,21 +157,13 @@ async def _read_file(
     match path.suffix:
         case ".csv" | ".tsv":
             kwargs.setdefault("sep", "," if path.suffix == ".csv" else "\t")
-            async with aiofiles.open(path) as f:
-                content = await f.read()
+            with path.open("r") as i_stream:
+                content = i_stream.read()
                 return pd.read_csv(io.StringIO(content), **kwargs)
         case ".xlsx" | ".xls":
-            loop = asyncio.get_running_loop()
-            with ThreadPoolExecutor(max_workers=1) as pool:
-                # pass kwargs as args
-                func = functools.partial(pd.read_excel, **kwargs)
-                return await loop.run_in_executor(pool, func, path)
+            return pd.read_excel(path, **kwargs)
         case ".h5ad":
-            loop = asyncio.get_running_loop()
-            with ThreadPoolExecutor(max_workers=1) as pool:
-                func = functools.partial(sc.read_h5ad, **kwargs)
-                adata: sc.AnnData = await loop.run_in_executor(pool, func, path)
-
+            adata: sc.AnnData = sc.read_h5ad(path, **kwargs)
             if h5ad_as_df:
                 df = adata.to_df().T
                 df.index.name = "gene_symbol"

From 13aa4aece7cf177e1572f446fbada482ce0ccf3d Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 31 Jan 2025 13:37:30 -0600
Subject: [PATCH 283/345] style: ruff formatting

---
 main/como/cluster_rnaseq.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/main/como/cluster_rnaseq.py b/main/como/cluster_rnaseq.py
index 818a45bc..2fb96cdc 100644
--- a/main/como/cluster_rnaseq.py
+++ b/main/como/cluster_rnaseq.py
@@ -36,7 +36,9 @@ def __post_init__(self):  # noqa: C901, ignore too complex
 
         if (isdigit(self.min_active_count) and int(self.min_active_count) < 0) or self.min_active_count != "default":
             _log_and_raise_error(
-                "min_active_count must be either 'default' or an integer > 0", error=ValueError, level=LogLevel.ERROR
+                "min_active_count must be either 'default' or an integer > 0",
+                error=ValueError,
+                level=LogLevel.ERROR,
             )
 
         if (isdigit(self.quantile) and 0 > int(self.quantile) > 100) or self.quantile != "default":

From 1ac47f0bf6e9241d871ce0ae203bc24ab256d70c Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 31 Jan 2025 13:39:20 -0600
Subject: [PATCH 284/345] fix: make parent directories before saving files

---
 main/como/create_context_specific_model.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index a375c815..eb5796fc 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -549,6 +549,7 @@ async def _collect_boundary_reactions(path: Path) -> _BoundaryReactions:
 
 def _write_model_to_disk(context_name: str, model: cobra.Model, output_filepaths: list[Path]) -> None:
     for path in output_filepaths:
+        path.parent.mkdir(parents=True, exist_ok=True)
         if path.suffix == ".mat":
             cobra.io.save_matlab_model(model=model, file_name=path)
         elif path.suffix == ".json":

From 72c363002c6ef254447ca716a0f253b53f20252e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 31 Jan 2025 13:40:43 -0600
Subject: [PATCH 285/345] refactor: use critical log instead of warning

---
 main/como/rnaseq_preprocess.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 12a5b82a..40e8a01d 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -533,8 +533,11 @@ async def read_counts(file: Path) -> list[str]:
                 else gene_symbol_to_ensembl_and_gene_id(symbols=data.var_names.tolist(), taxon=taxon)
             )
         except json.JSONDecodeError:
-            logger.warning(f"Got a JSON decode error for file '{counts_matrix_filepaths}', exiting")
-            exit()
+            _log_and_raise_error(
+                f"Got a JSO decode error for file '{counts_matrix_filepaths}'",
+                error=ValueError,
+                level=LogLevel.CRITICAL,
+            )
 
         # Remove NA values from entrez_gene_id dataframe column
         return conversion["entrez_gene_id"].dropna().tolist()

From 8be741d1628578bf5636d234a01efbeb3aa7ecab Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 31 Jan 2025 13:43:14 -0600
Subject: [PATCH 286/345] refactor: reset exection output and count

---
 main/COMO.ipynb | 98 ++++++++-----------------------------------------
 1 file changed, 15 insertions(+), 83 deletions(-)

diff --git a/main/COMO.ipynb b/main/COMO.ipynb
index 82647f6e..f86b9bd1 100644
--- a/main/COMO.ipynb
+++ b/main/COMO.ipynb
@@ -233,12 +233,14 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-12-16T20:09:29.761117Z",
      "start_time": "2024-12-16T20:09:29.754605Z"
     }
    },
+   "outputs": [],
    "source": [
     "from pathlib import Path\n",
     "\n",
@@ -265,9 +267,7 @@
     "proteomics_metadata_filepath = Path(\"data/config_sheets/proteomics_config.xlsx\")\n",
     "\n",
     "# Output Components\n"
-   ],
-   "outputs": [],
-   "execution_count": 11
+   ]
   },
   {
    "cell_type": "markdown",
@@ -282,12 +282,14 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-12-16T20:07:08.331063Z",
      "start_time": "2024-12-16T20:06:10.188429Z"
     }
    },
+   "outputs": [],
    "source": [
     "for context in context_names:\n",
     "    if context not in {*trna_matrix_filepath, *mrna_matrix_filepath}:\n",
@@ -305,20 +307,7 @@
     "        cache=True,\n",
     "        log_level=\"INFO\",\n",
     "    )"
-   ],
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001B[32m2024-12-16 14:06:13\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m277\u001B[0m - \u001B[32m\u001B[1mWrote gene count matrix for 'mrna' RNA at '/Users/joshl/Projects/COMO/main/data/results/naiveB/polya-rna/polyarna_naiveB.csv'\u001B[0m\n",
-      "\u001B[32m2024-12-16 14:06:13\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m277\u001B[0m - \u001B[32m\u001B[1mWrote gene count matrix for 'total' RNA at '/Users/joshl/Projects/COMO/main/data/results/naiveB/total-rna/totalrna_naiveB.csv'\u001B[0m\n",
-      "\u001B[32m2024-12-16 14:06:13\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m468\u001B[0m - \u001B[1mFetching gene info (this may take 1-5 minutes depending on the number of genes and your internet connection)\u001B[0m\n",
-      "\u001B[32m2024-12-16 14:07:08\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_preprocess\u001B[0m:\u001B[36m505\u001B[0m - \u001B[32m\u001B[1mGene Info file written at '/Users/joshl/Projects/COMO/main/data/results/naiveB/gene_info.csv'\u001B[0m\n"
-     ]
-    }
-   ],
-   "execution_count": 8
+   ]
   },
   {
    "cell_type": "markdown",
@@ -392,12 +381,14 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-12-16T20:08:00.274368Z",
      "start_time": "2024-12-16T20:07:48.637980Z"
     }
    },
+   "outputs": [],
    "source": [
     "from como.rnaseq_gen import FilteringTechnique, rnaseq_gen\n",
     "\n",
@@ -427,20 +418,7 @@
     "        technique=technique,\n",
     "        cutoff=cutoff\n",
     "    )"
-   ],
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/joshl/Projects/COMO/main/como/rnaseq_gen.py:430: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  mega_df = pd.concat([mega_df, df], ignore_index=True)\n",
-      "\u001B[32m2024-12-16 14:08:00\u001B[0m | \u001B[1mINFO    \u001B[0m | \u001B[36mcomo.rnaseq_gen\u001B[0m:\u001B[36m691\u001B[0m - \u001B[1mnaiveB - Found 9492 expressed and 9492 confidently expressed genes\u001B[0m\n",
-      "\u001B[32m2024-12-16 14:08:00\u001B[0m | \u001B[32m\u001B[1mSUCCESS \u001B[0m | \u001B[36mcomo.rnaseq_gen\u001B[0m:\u001B[36m694\u001B[0m - \u001B[32m\u001B[1mWrote boolean matrix to data/results/naiveB/total-rna/totalrna_naiveB.csv\u001B[0m\n"
-     ]
-    }
-   ],
-   "execution_count": 9
+   ]
   },
   {
    "cell_type": "markdown",
@@ -463,12 +441,14 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-12-16T20:15:16.139743Z",
      "start_time": "2024-12-16T20:15:15.998158Z"
     }
    },
+   "outputs": [],
    "source": [
     "from como.rnaseq_gen import FilteringTechnique, rnaseq_gen\n",
     "\n",
@@ -497,34 +477,7 @@
     "        technique=technique,\n",
     "        cutoff=cutoff\n",
     "    )"
-   ],
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'ensembl_gene_id'",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[0;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
-      "File \u001B[0;32m~/Projects/COMO/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3805\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[0;34m(self, key)\u001B[0m\n\u001B[1;32m   3804\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m-> 3805\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_engine\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcasted_key\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   3806\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n",
-      "File \u001B[0;32mindex.pyx:167\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[0;34m()\u001B[0m\n",
-      "File \u001B[0;32mindex.pyx:196\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[0;34m()\u001B[0m\n",
-      "File \u001B[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[0;34m()\u001B[0m\n",
-      "File \u001B[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[0;34m()\u001B[0m\n",
-      "\u001B[0;31mKeyError\u001B[0m: 'ensembl_gene_id'",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001B[0;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
-      "Cell \u001B[0;32mIn[15], line 13\u001B[0m\n\u001B[1;32m     11\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m context \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m mrna_matrix_filepath:\n\u001B[1;32m     12\u001B[0m     \u001B[38;5;28;01mcontinue\u001B[39;00m\n\u001B[0;32m---> 13\u001B[0m \u001B[38;5;28;01mawait\u001B[39;00m rnaseq_gen(\n\u001B[1;32m     14\u001B[0m     context_name\u001B[38;5;241m=\u001B[39mcontext,\n\u001B[1;32m     15\u001B[0m     input_rnaseq_filepath\u001B[38;5;241m=\u001B[39mmrna_matrix_filepath[context],\n\u001B[1;32m     16\u001B[0m     input_gene_info_filepath\u001B[38;5;241m=\u001B[39mgene_info_filepath[context],\n\u001B[1;32m     17\u001B[0m     output_rnaseq_filepath\u001B[38;5;241m=\u001B[39mmrna_matrix_filepath[context],\n\u001B[1;32m     18\u001B[0m     prep\u001B[38;5;241m=\u001B[39mRNAPrepMethod\u001B[38;5;241m.\u001B[39mMRNA,\n\u001B[1;32m     19\u001B[0m     taxon\u001B[38;5;241m=\u001B[39mtaxon_id,\n\u001B[1;32m     20\u001B[0m     input_metadata_filepath\u001B[38;5;241m=\u001B[39mmrna_metadata_filepath,\n\u001B[1;32m     21\u001B[0m     replicate_ratio\u001B[38;5;241m=\u001B[39mreplicate_ratio,\n\u001B[1;32m     22\u001B[0m     high_replicate_ratio\u001B[38;5;241m=\u001B[39mhigh_confidence_replicate_ratio,\n\u001B[1;32m     23\u001B[0m     batch_ratio\u001B[38;5;241m=\u001B[39mbatch_ratio,\n\u001B[1;32m     24\u001B[0m     high_batch_ratio\u001B[38;5;241m=\u001B[39mhigh_confidence_batch_ratio,\n\u001B[1;32m     25\u001B[0m     technique\u001B[38;5;241m=\u001B[39mtechnique,\n\u001B[1;32m     26\u001B[0m     cutoff\u001B[38;5;241m=\u001B[39mcutoff\n\u001B[1;32m     27\u001B[0m )\n",
-      "File \u001B[0;32m~/Projects/COMO/main/como/rnaseq_gen.py:787\u001B[0m, in \u001B[0;36mrnaseq_gen\u001B[0;34m(context_name, input_rnaseq_filepath, input_gene_info_filepath, output_rnaseq_filepath, prep, taxon, input_metadata_filepath, input_metadata_df, replicate_ratio, high_replicate_ratio, batch_ratio, high_batch_ratio, technique, cutoff)\u001B[0m\n\u001B[1;32m    784\u001B[0m logger\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mStarting \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext_name\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m    785\u001B[0m output_rnaseq_filepath\u001B[38;5;241m.\u001B[39mparent\u001B[38;5;241m.\u001B[39mmkdir(parents\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, exist_ok\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[0;32m--> 787\u001B[0m \u001B[38;5;28;01mawait\u001B[39;00m _save_rnaseq_tests(\n\u001B[1;32m    788\u001B[0m     context_name\u001B[38;5;241m=\u001B[39mcontext_name,\n\u001B[1;32m    789\u001B[0m     rnaseq_matrix\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mawait\u001B[39;00m _read_counts(input_rnaseq_filepath),\n\u001B[1;32m    790\u001B[0m     metadata_df\u001B[38;5;241m=\u001B[39minput_metadata_df \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28;01mawait\u001B[39;00m _create_metadata_df(input_metadata_filepath),\n\u001B[1;32m    791\u001B[0m     gene_info_df\u001B[38;5;241m=\u001B[39mpd\u001B[38;5;241m.\u001B[39mread_csv(input_gene_info_filepath),\n\u001B[1;32m    792\u001B[0m     output_filepath\u001B[38;5;241m=\u001B[39moutput_rnaseq_filepath,\n\u001B[1;32m    793\u001B[0m     prep\u001B[38;5;241m=\u001B[39mprep,\n\u001B[1;32m    794\u001B[0m     taxon\u001B[38;5;241m=\u001B[39mtaxon,\n\u001B[1;32m    795\u001B[0m     replicate_ratio\u001B[38;5;241m=\u001B[39mreplicate_ratio,\n\u001B[1;32m    796\u001B[0m     batch_ratio\u001B[38;5;241m=\u001B[39mbatch_ratio,\n\u001B[1;32m    797\u001B[0m     high_replicate_ratio\u001B[38;5;241m=\u001B[39mhigh_replicate_ratio,\n\u001B[1;32m    798\u001B[0m     high_batch_ratio\u001B[38;5;241m=\u001B[39mhigh_batch_ratio,\n\u001B[1;32m    799\u001B[0m     technique\u001B[38;5;241m=\u001B[39mtechnique,\n\u001B[1;32m    800\u001B[0m     cut_off\u001B[38;5;241m=\u001B[39mcutoff,\n\u001B[1;32m    801\u001B[0m )\n",
-      "File \u001B[0;32m~/Projects/COMO/main/como/rnaseq_gen.py:645\u001B[0m, in \u001B[0;36m_save_rnaseq_tests\u001B[0;34m(context_name, rnaseq_matrix, metadata_df, gene_info_df, output_filepath, prep, taxon, replicate_ratio, batch_ratio, high_replicate_ratio, high_batch_ratio, technique, cut_off)\u001B[0m\n\u001B[1;32m    636\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Save the results of the RNA-Seq tests to a CSV file.\"\"\"\u001B[39;00m\n\u001B[1;32m    637\u001B[0m filtering_options \u001B[38;5;241m=\u001B[39m _FilteringOptions(\n\u001B[1;32m    638\u001B[0m     replicate_ratio\u001B[38;5;241m=\u001B[39mreplicate_ratio,\n\u001B[1;32m    639\u001B[0m     batch_ratio\u001B[38;5;241m=\u001B[39mbatch_ratio,\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    642\u001B[0m     high_batch_ratio\u001B[38;5;241m=\u001B[39mhigh_batch_ratio,\n\u001B[1;32m    643\u001B[0m )\n\u001B[0;32m--> 645\u001B[0m read_counts_results: _ReadMatrixResults \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m _build_matrix_results(\n\u001B[1;32m    646\u001B[0m     matrix\u001B[38;5;241m=\u001B[39mrnaseq_matrix,\n\u001B[1;32m    647\u001B[0m     gene_info\u001B[38;5;241m=\u001B[39mgene_info_df,\n\u001B[1;32m    648\u001B[0m     metadata_df\u001B[38;5;241m=\u001B[39mmetadata_df,\n\u001B[1;32m    649\u001B[0m     taxon\u001B[38;5;241m=\u001B[39mtaxon,\n\u001B[1;32m    650\u001B[0m )\n\u001B[1;32m    651\u001B[0m metrics \u001B[38;5;241m=\u001B[39m read_counts_results\u001B[38;5;241m.\u001B[39mmetrics\n\u001B[1;32m    652\u001B[0m entrez_gene_ids \u001B[38;5;241m=\u001B[39m read_counts_results\u001B[38;5;241m.\u001B[39mentrez_gene_ids\n",
-      "File \u001B[0;32m~/Projects/COMO/main/como/rnaseq_gen.py:175\u001B[0m, in \u001B[0;36m_build_matrix_results\u001B[0;34m(matrix, gene_info, metadata_df, taxon)\u001B[0m\n\u001B[1;32m    167\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Read the counts matrix and returns the results.\u001B[39;00m\n\u001B[1;32m    168\u001B[0m \n\u001B[1;32m    169\u001B[0m \u001B[38;5;124;03m:param matrix: The gene counts matrix to process\u001B[39;00m\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    172\u001B[0m \u001B[38;5;124;03m:return: A dataclass `ReadMatrixResults`\u001B[39;00m\n\u001B[1;32m    173\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m    174\u001B[0m gene_info \u001B[38;5;241m=\u001B[39m gene_info_migrations(gene_info)\n\u001B[0;32m--> 175\u001B[0m conversion \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mawait\u001B[39;00m ensembl_to_gene_id_and_symbol(ids\u001B[38;5;241m=\u001B[39m\u001B[43mmatrix\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mensembl_gene_id\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[38;5;241m.\u001B[39mtolist(), taxon\u001B[38;5;241m=\u001B[39mtaxon)\n\u001B[1;32m    176\u001B[0m matrix \u001B[38;5;241m=\u001B[39m matrix\u001B[38;5;241m.\u001B[39mmerge(conversion, on\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mensembl_gene_id\u001B[39m\u001B[38;5;124m\"\u001B[39m, how\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mleft\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m    178\u001B[0m \u001B[38;5;66;03m# Only include Entrez and Ensembl Gene IDs that are present in `gene_info`\u001B[39;00m\n",
-      "File \u001B[0;32m~/Projects/COMO/.venv/lib/python3.10/site-packages/pandas/core/frame.py:4102\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[0;34m(self, key)\u001B[0m\n\u001B[1;32m   4100\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcolumns\u001B[38;5;241m.\u001B[39mnlevels \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[1;32m   4101\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_getitem_multilevel(key)\n\u001B[0;32m-> 4102\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcolumns\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_loc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mkey\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   4103\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_integer(indexer):\n\u001B[1;32m   4104\u001B[0m     indexer \u001B[38;5;241m=\u001B[39m [indexer]\n",
-      "File \u001B[0;32m~/Projects/COMO/.venv/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[0;34m(self, key)\u001B[0m\n\u001B[1;32m   3807\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(casted_key, \u001B[38;5;28mslice\u001B[39m) \u001B[38;5;129;01mor\u001B[39;00m (\n\u001B[1;32m   3808\u001B[0m         \u001B[38;5;28misinstance\u001B[39m(casted_key, abc\u001B[38;5;241m.\u001B[39mIterable)\n\u001B[1;32m   3809\u001B[0m         \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28many\u001B[39m(\u001B[38;5;28misinstance\u001B[39m(x, \u001B[38;5;28mslice\u001B[39m) \u001B[38;5;28;01mfor\u001B[39;00m x \u001B[38;5;129;01min\u001B[39;00m casted_key)\n\u001B[1;32m   3810\u001B[0m     ):\n\u001B[1;32m   3811\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m InvalidIndexError(key)\n\u001B[0;32m-> 3812\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[1;32m   3813\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[1;32m   3814\u001B[0m     \u001B[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001B[39;00m\n\u001B[1;32m   3815\u001B[0m     \u001B[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001B[39;00m\n\u001B[1;32m   3816\u001B[0m     \u001B[38;5;66;03m#  the TypeError.\u001B[39;00m\n\u001B[1;32m   3817\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_indexing_error(key)\n",
-      "\u001B[0;31mKeyError\u001B[0m: 'ensembl_gene_id'"
-     ]
-    }
-   ],
-   "execution_count": 15
+   ]
   },
   {
    "cell_type": "markdown",
@@ -731,12 +684,14 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-12-16T19:54:26.540397Z",
      "start_time": "2024-12-16T19:54:26.515554Z"
     }
    },
+   "outputs": [],
    "source": [
     "from como.merge_xomics import AdjustmentMethod, merge_xomics\n",
     "\n",
@@ -791,30 +746,7 @@
     "        output_final_model_scores_filepath=Path(f\"data/results/{context}/model_scores_{context}.csv\"),\n",
     "        output_figure_dirpath=Path(f\"data/results/{context}/figures\")\n",
     "    )"
-   ],
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "data/results/naiveB/total-rna/trna_activity_naiveB.csv\n"
-     ]
-    },
-    {
-     "ename": "FileExistsError",
-     "evalue": "[Errno 17] File exists: 'data/results/naiveB/total-rna'",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[0;31mFileExistsError\u001B[0m                           Traceback (most recent call last)",
-      "Cell \u001B[0;32mIn[6], line 24\u001B[0m\n\u001B[1;32m     20\u001B[0m trna_batches \u001B[38;5;241m=\u001B[39m {\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnaiveB\u001B[39m\u001B[38;5;124m\"\u001B[39m: [\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnaiveB_S3R1\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnaiveB_S3R2\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnaiveB_S3R3\u001B[39m\u001B[38;5;124m\"\u001B[39m]}\n\u001B[1;32m     23\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m context \u001B[38;5;129;01min\u001B[39;00m context_names:\n\u001B[0;32m---> 24\u001B[0m     \u001B[38;5;28;01mawait\u001B[39;00m merge_xomics(\n\u001B[1;32m     25\u001B[0m         context_name\u001B[38;5;241m=\u001B[39mcontext_names,\n\u001B[1;32m     26\u001B[0m         trna_matrix_or_filepath\u001B[38;5;241m=\u001B[39mtrna_matrix_filepath[context],\n\u001B[1;32m     27\u001B[0m         mrna_matrix_or_filepath\u001B[38;5;241m=\u001B[39mmrna_matrix_filepath[context],\n\u001B[1;32m     28\u001B[0m         scrna_matrix_or_filepath\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,  \u001B[38;5;66;03m# scrna_matrix_filepath[context],\u001B[39;00m\n\u001B[1;32m     29\u001B[0m         proteomic_matrix_or_filepath\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,  \u001B[38;5;66;03m# proteomics_matrix_filepath[context],\u001B[39;00m\n\u001B[1;32m     30\u001B[0m         trna_batches\u001B[38;5;241m=\u001B[39mtrna_batches,\n\u001B[1;32m     31\u001B[0m         mrna_batches\u001B[38;5;241m=\u001B[39mmrna_batches,\n\u001B[1;32m     32\u001B[0m         scrna_batches\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[1;32m     33\u001B[0m         proteomic_batches\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[1;32m     34\u001B[0m         trna_weight\u001B[38;5;241m=\u001B[39mtotal_rna_weight,\n\u001B[1;32m     35\u001B[0m         mrna_weight\u001B[38;5;241m=\u001B[39mmrna_weight,\n\u001B[1;32m     36\u001B[0m         scrna_weight\u001B[38;5;241m=\u001B[39msingle_cell_weight,\n\u001B[1;32m     37\u001B[0m         proteomic_weight\u001B[38;5;241m=\u001B[39mproteomics_weight,\n\u001B[1;32m     38\u001B[0m         minimum_source_expression\u001B[38;5;241m=\u001B[39mminimum_source_expression,\n\u001B[1;32m     39\u001B[0m         expression_requirement\u001B[38;5;241m=\u001B[39mexpression_requirement,\n\u001B[1;32m     40\u001B[0m         adjust_method\u001B[38;5;241m=\u001B[39mrequirement_adjustment_method,\n\u001B[1;32m     41\u001B[0m         force_activate_high_confidence\u001B[38;5;241m=\u001B[39mforce_activate_high_confidence,\n\u001B[1;32m     42\u001B[0m         adjust_for_na\u001B[38;5;241m=\u001B[39madjust_for_na_sources,\n\u001B[1;32m     43\u001B[0m         merge_zfpkm_distribution\u001B[38;5;241m=\u001B[39mmerge_zfpkm_distrubution,\n\u001B[1;32m     44\u001B[0m         keep_transcriptomics_score\u001B[38;5;241m=\u001B[39mkeep_transcriptomics_score,\n\u001B[1;32m     45\u001B[0m         output_merge_activity_filepath\u001B[38;5;241m=\u001B[39mPath(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdata/results/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/ActiveGenes_\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m_Merged.csv\u001B[39m\u001B[38;5;124m\"\u001B[39m),\n\u001B[1;32m     46\u001B[0m         output_transcriptomic_details_filepath\u001B[38;5;241m=\u001B[39mPath(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdata/results/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/TranscriptomicDetails_\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.csv\u001B[39m\u001B[38;5;124m\"\u001B[39m),\n\u001B[1;32m     47\u001B[0m         output_trna_activity_filepath\u001B[38;5;241m=\u001B[39mPath(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdata/results/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/total-rna/trna_activity_\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.csv\u001B[39m\u001B[38;5;124m\"\u001B[39m),\n\u001B[1;32m     48\u001B[0m         output_mrna_activity_filepath\u001B[38;5;241m=\u001B[39mPath(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdata/results/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/mrna/mrna_activity_\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.csv\u001B[39m\u001B[38;5;124m\"\u001B[39m),\n\u001B[1;32m     49\u001B[0m         output_scrna_activity_filepath\u001B[38;5;241m=\u001B[39mPath(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdata/results/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/scrna/scrna_activity_\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.csv\u001B[39m\u001B[38;5;124m\"\u001B[39m),\n\u001B[1;32m     50\u001B[0m         output_proteomic_activity_filepath\u001B[38;5;241m=\u001B[39mPath(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdata/results/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/proteomics/proteomic_activity_\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.csv\u001B[39m\u001B[38;5;124m\"\u001B[39m),\n\u001B[1;32m     51\u001B[0m         output_final_model_scores_filepath\u001B[38;5;241m=\u001B[39mPath(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdata/results/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/model_scores_\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m.csv\u001B[39m\u001B[38;5;124m\"\u001B[39m),\n\u001B[1;32m     52\u001B[0m         output_figure_dirpath\u001B[38;5;241m=\u001B[39mPath(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdata/results/\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcontext\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m/figures\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m     53\u001B[0m     )\n",
-      "File \u001B[0;32m~/Projects/COMO/main/como/merge_xomics.py:678\u001B[0m, in \u001B[0;36mmerge_xomics\u001B[0;34m(context_name, trna_matrix_or_filepath, mrna_matrix_or_filepath, scrna_matrix_or_filepath, proteomic_matrix_or_filepath, trna_batches, mrna_batches, scrna_batches, proteomic_batches, output_merge_activity_filepath, output_transcriptomic_details_filepath, output_trna_activity_filepath, output_mrna_activity_filepath, output_scrna_activity_filepath, output_proteomic_activity_filepath, output_final_model_scores_filepath, output_figure_dirpath, trna_weight, mrna_weight, scrna_weight, proteomic_weight, minimum_source_expression, expression_requirement, adjust_method, force_activate_high_confidence, adjust_for_na, merge_zfpkm_distribution, keep_transcriptomics_score, weighted_z_floor, weighted_z_ceiling)\u001B[0m\n\u001B[1;32m    676\u001B[0m     output_transcriptomic_details_filepath\u001B[38;5;241m.\u001B[39mparent\u001B[38;5;241m.\u001B[39mmkdir(parents\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[1;32m    677\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m output_trna_activity_filepath \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m output_trna_activity_filepath\u001B[38;5;241m.\u001B[39mexists():\n\u001B[0;32m--> 678\u001B[0m     \u001B[43moutput_trna_activity_filepath\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mparent\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmkdir\u001B[49m\u001B[43m(\u001B[49m\u001B[43mparents\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[1;32m    679\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m output_mrna_activity_filepath \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m output_mrna_activity_filepath\u001B[38;5;241m.\u001B[39mexists():\n\u001B[1;32m    680\u001B[0m     output_mrna_activity_filepath\u001B[38;5;241m.\u001B[39mparent\u001B[38;5;241m.\u001B[39mmkdir(parents\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n",
-      "File \u001B[0;32m~/.local/share/uv/python/cpython-3.10.15-macos-aarch64-none/lib/python3.10/pathlib.py:1175\u001B[0m, in \u001B[0;36mPath.mkdir\u001B[0;34m(self, mode, parents, exist_ok)\u001B[0m\n\u001B[1;32m   1171\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m   1172\u001B[0m \u001B[38;5;124;03mCreate a new directory at this given path.\u001B[39;00m\n\u001B[1;32m   1173\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m   1174\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m-> 1175\u001B[0m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_accessor\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmkdir\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmode\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   1176\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mFileNotFoundError\u001B[39;00m:\n\u001B[1;32m   1177\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m parents \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mparent \u001B[38;5;241m==\u001B[39m \u001B[38;5;28mself\u001B[39m:\n",
-      "\u001B[0;31mFileExistsError\u001B[0m: [Errno 17] File exists: 'data/results/naiveB/total-rna'"
-     ]
-    }
-   ],
-   "execution_count": 6
+   ]
   },
   {
    "cell_type": "markdown",

From 35a0bf239a6b88c651fb2c8fc186ddbc20d28c17 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 31 Jan 2025 13:44:55 -0600
Subject: [PATCH 287/345] fix: rename column names

---
 main/como/create_context_specific_model.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index eb5796fc..1860745e 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -508,7 +508,7 @@ async def _collect_boundary_reactions(path: Path) -> _BoundaryReactions:
     df: pd.DataFrame = await _create_df(path)
     for column in df.columns:
         if column not in [
-            "boundary",
+            "reaction",
             "abbreviation",
             "compartment",
             "minimum reaction rate",
@@ -524,7 +524,7 @@ async def _collect_boundary_reactions(path: Path) -> _BoundaryReactions:
             )
 
     reactions: list[str] = [""] * len(df)
-    boundary_type: list[str] = df["boundary"].tolist()
+    boundary_type: list[str] = df["reaction"].tolist()
     reaction_abbreviation: list[str] = df["abbreviation"].astype(str).tolist()
     reaction_compartment: list[str] = df["compartment"].astype(str).tolist()
     boundary_map = {"exchange": "EX", "demand": "DM", "sink": "SK"}

From 13f325393c8778d330659663e9fe12e3cb169038 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 3 Jun 2025 16:09:45 -0500
Subject: [PATCH 288/345] chore: increase line length

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 ruff.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ruff.toml b/ruff.toml
index eeea6f84..309140fa 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -1,4 +1,4 @@
-line-length = 120
+line-length = 150
 extend-include = ["docs/**/*.py", "tests/**/*.py", "**/*.ipynb"]
 
 [format]

From 12aa4ad6d9aaf2c4e3c88130c5d41f921b5eb3a5 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 3 Jun 2025 16:11:28 -0500
Subject: [PATCH 289/345] refactor: updated to be more python-like

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/COMO.ipynb | 208 +++++++++++++++++++++++++++++++++++++-----------
 1 file changed, 161 insertions(+), 47 deletions(-)

diff --git a/main/COMO.ipynb b/main/COMO.ipynb
index f86b9bd1..6c2b24fb 100644
--- a/main/COMO.ipynb
+++ b/main/COMO.ipynb
@@ -2,11 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
+   "metadata": {},
    "source": [
     "# COMO: Constraint-based Optomization of Metabolic Objectives\n",
     "\n",
@@ -60,11 +56,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
+   "metadata": {},
    "source": [
     "# Step 1: Data Preprocessing and Analysis\n",
     "\n",
@@ -233,14 +225,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-12-16T20:09:29.761117Z",
-     "start_time": "2024-12-16T20:09:29.754605Z"
+     "end_time": "2025-05-14T22:26:41.582304Z",
+     "start_time": "2025-05-14T22:26:41.578323Z"
     }
    },
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "\n",
+    "def get_notebook_dir() -> Path:\n",
+    "    \"\"\"Get the directory of the COMO.ipynb notebook.\n",
+    "\n",
+    "    This is used to set the proper input and output paths for the rest of the notebook\n",
+    "    \"\"\"\n",
+    "    current_dir = Path().cwd()\n",
+    "\n",
+    "    while True:\n",
+    "        files = [i.name for i in current_dir.iterdir()]\n",
+    "        if \"COMO.ipynb\" in files:\n",
+    "            return current_dir\n",
+    "        if current_dir.as_posix() == \"/\":\n",
+    "            raise FileNotFoundError(\"Root directory could not be determined; unable to find 'COMO.ipynb'\")\n",
+    "        current_dir = current_dir.parent"
+   ],
    "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-05-14T22:38:52.351270Z",
+     "start_time": "2025-05-14T22:38:52.343987Z"
+    }
+   },
    "source": [
     "from pathlib import Path\n",
     "\n",
@@ -248,26 +268,37 @@
     "from como.rnaseq_preprocess import rnaseq_preprocess\n",
     "\n",
     "taxon_id = 9606\n",
-    "context_names = [\"naiveB\"]\n",
+    "# context_names = [\"control1hr\", \"control6hr\", \"control15min\", \"control24hr\", \"indralin1hr\", \"indralin6hr\",\n",
+    "#                  \"indralin15min\", \"indralin24hr\", \"notreatment\"]\n",
+    "context_names = [\"notreatment\"]\n",
+    "notebook_dir = get_notebook_dir()\n",
     "\n",
     "# Input Components\n",
-    "como_context_dir = {context: Path(f\"data/COMO_input/{context}\") for context in context_names}\n",
-    "gene_info_filepath = {context: Path(f\"data/results/{context}/gene_info.csv\") for context in context_names}\n",
-    "trna_matrix_filepath = {context: Path(f\"data/results/{context}/total-rna/totalrna_{context}.csv\") for context in context_names}\n",
-    "mrna_matrix_filepath = {context: Path(f\"data/results/{context}/mrna/mrna_{context}.csv\") for context in context_names}\n",
-    "proteomics_matrix_filepath = {context: Path(f\"data/data_matrices/{context}/protein_abundance_{context}.csv\") for context in context_names}\n",
+    "como_context_dir = {context: Path(notebook_dir / f\"data/COMO_input/{context}\") for context in context_names}\n",
+    "gene_info_filepath = {context: Path(notebook_dir / f\"data/results/{context}/gene_info.csv\") for context in\n",
+    "                      context_names}\n",
+    "trna_matrix_filepath = {context: Path(notebook_dir / f\"data/results/{context}/total-rna/totalrna_{context}.csv\") for\n",
+    "                        context in\n",
+    "                        context_names}\n",
+    "mrna_matrix_filepath = {context: Path(notebook_dir / f\"data/results/{context}/mrna/mrna_{context}.csv\") for context in\n",
+    "                        context_names}\n",
+    "proteomics_matrix_filepath = {\n",
+    "    context: Path(notebook_dir / f\"data/data_matrices/{context}/protein_abundance_{context}.csv\") for context\n",
+    "    in context_names}\n",
     "\n",
     "# No single-cell data is provided by default; COMO accepts single-cell data in CSV or h5ad format\n",
     "# If you are using single-cell data, adjust the following lines accordingly\n",
-    "scrna_matrix_filepath = {context: Path(f\"data/results/{context}/scrna/scrna_{context}.csv\") for context in context_names}\n",
+    "scrna_matrix_filepath = {context: Path(notebook_dir / f\"data/results/{context}/scrna/scrna_{context}.csv\") for context\n",
+    "                         in\n",
+    "                         context_names}\n",
     "# scrna_matrix_filepath = [Path(f\"data/results/{context}/scrna/scrna_{context}.h5ad\") for context in context_names]\n",
     "\n",
-    "trna_metadata_filepath = Path(\"data/config_sheets/trna_config.xlsx\")\n",
-    "mrna_metadata_filepath = Path(\"data/config_sheets/mrna_config.xlsx\")\n",
-    "proteomics_metadata_filepath = Path(\"data/config_sheets/proteomics_config.xlsx\")\n",
-    "\n",
-    "# Output Components\n"
-   ]
+    "trna_metadata_filepath = Path(notebook_dir / \"data/config_sheets/trna_config.xlsx\")\n",
+    "mrna_metadata_filepath = Path(notebook_dir / \"data/config_sheets/mrna_config.xlsx\")\n",
+    "proteomics_metadata_filepath = Path(notebook_dir / \"data/config_sheets/proteomics_config.xlsx\")\n"
+   ],
+   "outputs": [],
+   "execution_count": 4
   },
   {
    "cell_type": "markdown",
@@ -282,14 +313,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {
+    "jupyter": {
+     "is_executing": true
+    },
     "ExecuteTime": {
-     "end_time": "2024-12-16T20:07:08.331063Z",
-     "start_time": "2024-12-16T20:06:10.188429Z"
+     "start_time": "2025-05-14T22:39:41.777900Z"
     }
    },
-   "outputs": [],
    "source": [
     "for context in context_names:\n",
     "    if context not in {*trna_matrix_filepath, *mrna_matrix_filepath}:\n",
@@ -302,12 +333,32 @@
     "        output_gene_info_filepath=gene_info_filepath[context],\n",
     "        output_trna_metadata_filepath=trna_metadata_filepath,\n",
     "        output_trna_count_matrix_filepath=trna_matrix_filepath[context],\n",
-    "        output_mrna_metadata_filepath=mrna_metadata_filepath,\n",
-    "        output_mrna_count_matrix_filepath=mrna_matrix_filepath[context],\n",
+    "        # output_mrna_metadata_filepath=mrna_metadata_filepath,\n",
+    "        # output_mrna_count_matrix_filepath=mrna_matrix_filepath[context],\n",
     "        cache=True,\n",
     "        log_level=\"INFO\",\n",
     "    )"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "notreatment\n",
+      "9606\n",
+      "/Users/joshl/Projects/COMO/main/data/COMO_input/notreatment\n",
+      "None\n",
+      "/Users/joshl/Projects/COMO/main/data/results/notreatment/gene_info.csv\n",
+      "/Users/joshl/Projects/COMO/main/data/config_sheets/trna_config.xlsx\n",
+      "/Users/joshl/Projects/COMO/main/data/results/notreatment/total-rna/totalrna_notreatment.csv\n",
+      "/Users/joshl/Projects/COMO/main/data/config_sheets/mrna_config.xlsx\n",
+      "/Users/joshl/Projects/COMO/main/data/results/notreatment/mrna/mrna_notreatment.csv\n",
+      "True\n",
+      "INFO\n"
+     ]
+    }
+   ],
+   "execution_count": null
   },
   {
    "cell_type": "markdown",
@@ -381,14 +432,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-12-16T20:08:00.274368Z",
-     "start_time": "2024-12-16T20:07:48.637980Z"
+     "end_time": "2025-05-14T22:26:45.087604Z",
+     "start_time": "2025-05-14T22:26:44.394120Z"
     }
    },
-   "outputs": [],
    "source": [
     "from como.rnaseq_gen import FilteringTechnique, rnaseq_gen\n",
     "\n",
@@ -399,10 +448,10 @@
     "technique = FilteringTechnique.ZFPKM\n",
     "cutoff = -3\n",
     "\n",
-    "\n",
     "for context in context_names:\n",
     "    if context not in trna_matrix_filepath:\n",
     "        continue\n",
+    "    output_zscore_norm_filepath = Path(get_notebook_dir() / f\"data/results/{context}/z_score_normalization.csv\")\n",
     "    await rnaseq_gen(\n",
     "        context_name=context,\n",
     "        input_rnaseq_filepath=trna_matrix_filepath[context],\n",
@@ -410,15 +459,84 @@
     "        output_boolean_activity_filepath=trna_matrix_filepath[context],\n",
     "        prep=RNAType.TRNA,\n",
     "        taxon_id=taxon_id,\n",
-    "        input_metadata_filepath=trna_metadata_filepath,\n",
+    "        input_metadata_filepath_or_df=trna_metadata_filepath,\n",
     "        replicate_ratio=replicate_ratio,\n",
     "        high_replicate_ratio=high_confidence_replicate_ratio,\n",
     "        batch_ratio=batch_ratio,\n",
     "        high_batch_ratio=high_confidence_batch_ratio,\n",
     "        technique=technique,\n",
-    "        cutoff=cutoff\n",
+    "        cutoff=cutoff,\n",
+    "        output_zscore_normalization_filepath=output_zscore_norm_filepath,\n",
     "    )"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          ensembl_gene_id  control1hr_S1R1  control1hr_S1R2  control1hr_S2R1  \\\n",
+      "0      ENSMUSG00000000001           1377.0           1373.0           1388.0   \n",
+      "1      ENSMUSG00000000003              0.0              0.0              0.0   \n",
+      "2      ENSMUSG00000000028            551.0            552.0            464.0   \n",
+      "3      ENSMUSG00000000031              1.0              5.0              1.0   \n",
+      "4      ENSMUSG00000000037             21.0              9.0             24.0   \n",
+      "...                   ...              ...              ...              ...   \n",
+      "77343  ENSMUSG00002076988              0.0              0.0              0.0   \n",
+      "77344  ENSMUSG00002076989              0.0              0.0              0.0   \n",
+      "77345  ENSMUSG00002076990              0.0              0.0              0.0   \n",
+      "77346  ENSMUSG00002076991              0.0              0.0              0.0   \n",
+      "77347  ENSMUSG00002076992              0.0              0.0              0.0   \n",
+      "\n",
+      "       control1hr_S2R2  control1hr_S3R1  control1hr_S3R2  control1hr_S4R1  \\\n",
+      "0               1393.0             1527           1476.0             1552   \n",
+      "1                  0.0                0              0.0                0   \n",
+      "2                537.0              656            653.0              677   \n",
+      "3                  1.0                0              0.0                0   \n",
+      "4                 18.0               28             19.0               33   \n",
+      "...                ...              ...              ...              ...   \n",
+      "77343              0.0                0              0.0                0   \n",
+      "77344              0.0                0              0.0                0   \n",
+      "77345              0.0                0              0.0                0   \n",
+      "77346              0.0                0              0.0                0   \n",
+      "77347              0.0                0              0.0                0   \n",
+      "\n",
+      "       control1hr_S4R2  \n",
+      "0                 1676  \n",
+      "1                    0  \n",
+      "2                  656  \n",
+      "3                    0  \n",
+      "4                   37  \n",
+      "...                ...  \n",
+      "77343                0  \n",
+      "77344                0  \n",
+      "77345                0  \n",
+      "77346                0  \n",
+      "77347                0  \n",
+      "\n",
+      "[77348 rows x 9 columns]\n"
+     ]
+    },
+    {
+     "ename": "KeyError",
+     "evalue": "'entrez_gene_id'",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[0;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
+      "\u001B[0;32m/var/folders/6s/9l9z74v90tn8lprrp7fchb9w0000gn/T/ipykernel_84074/4036172948.py\u001B[0m in \u001B[0;36m?\u001B[0;34m()\u001B[0m\n\u001B[1;32m     10\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0mcontext\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mcontext_names\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     11\u001B[0m     \u001B[0;32mif\u001B[0m \u001B[0mcontext\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mtrna_matrix_filepath\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     12\u001B[0m         \u001B[0;32mcontinue\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     13\u001B[0m     \u001B[0moutput_zscore_norm_filepath\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mPath\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mget_notebook_dir\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m/\u001B[0m \u001B[0;34mf\"\u001B[0m\u001B[0;34mdata/results/\u001B[0m\u001B[0;34m{\u001B[0m\u001B[0mcontext\u001B[0m\u001B[0;34m}\u001B[0m\u001B[0;34m/z_score_normalization.csv\u001B[0m\u001B[0;34m\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 14\u001B[0;31m     await rnaseq_gen(\n\u001B[0m\u001B[1;32m     15\u001B[0m         \u001B[0mcontext_name\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mcontext\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     16\u001B[0m         \u001B[0minput_rnaseq_filepath\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mtrna_matrix_filepath\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mcontext\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     17\u001B[0m         \u001B[0minput_gene_info_filepath\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mgene_info_filepath\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mcontext\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
+      "\u001B[0;32m~/Projects/COMO/main/como/rnaseq_gen.py\u001B[0m in \u001B[0;36m?\u001B[0;34m(context_name, input_rnaseq_filepath, input_gene_info_filepath, prep, taxon_id, output_boolean_activity_filepath, output_zscore_normalization_filepath, input_metadata_filepath_or_df, replicate_ratio, high_replicate_ratio, batch_ratio, high_batch_ratio, technique, zfpkm_peak_height, zfpkm_peak_distance, zfpkm_bandwidth, cutoff, force_zfpkm_plot, log_level, log_location, output_zfpkm_png_filepath)\u001B[0m\n\u001B[1;32m    933\u001B[0m             \u001B[0mlevel\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mLogLevel\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mERROR\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    934\u001B[0m         \u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    935\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    936\u001B[0m     \u001B[0mlogger\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdebug\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34mf\"\u001B[0m\u001B[0;34mStarting '\u001B[0m\u001B[0;34m{\u001B[0m\u001B[0mcontext_name\u001B[0m\u001B[0;34m}\u001B[0m\u001B[0;34m'\u001B[0m\u001B[0;34m\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 937\u001B[0;31m     await _process(\n\u001B[0m\u001B[1;32m    938\u001B[0m         \u001B[0mcontext_name\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mcontext_name\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    939\u001B[0m         \u001B[0mrnaseq_matrix_filepath\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0minput_rnaseq_filepath\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    940\u001B[0m         \u001B[0mmetadata_df\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mmetadata_df\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
+      "\u001B[0;32m~/Projects/COMO/main/como/rnaseq_gen.py\u001B[0m in \u001B[0;36m?\u001B[0;34m(context_name, rnaseq_matrix_filepath, metadata_df, gene_info_df, prep, taxon, replicate_ratio, batch_ratio, high_replicate_ratio, high_batch_ratio, technique, cut_off, force_zfpkm_plot, peak_parameters, bandwidth, output_boolean_activity_filepath, output_zscore_normalization_filepath, output_zfpkm_png_filepath)\u001B[0m\n\u001B[1;32m    723\u001B[0m         \u001B[0mhigh_replicate_ratio\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mhigh_replicate_ratio\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    724\u001B[0m         \u001B[0mhigh_batch_ratio\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mhigh_batch_ratio\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    725\u001B[0m     \u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    726\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 727\u001B[0;31m     read_counts_results: _ReadMatrixResults = await _build_matrix_results(\n\u001B[0m\u001B[1;32m    728\u001B[0m         \u001B[0mmatrix\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mrnaseq_matrix\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    729\u001B[0m         \u001B[0mgene_info\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mgene_info_df\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    730\u001B[0m         \u001B[0mmetadata_df\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mmetadata_df\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
+      "\u001B[0;32m~/Projects/COMO/main/como/rnaseq_gen.py\u001B[0m in \u001B[0;36m?\u001B[0;34m(matrix, gene_info, metadata_df, taxon)\u001B[0m\n\u001B[1;32m    169\u001B[0m     \u001B[0mconversion\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m\"ensembl_gene_id\"\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mconversion\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m\"ensembl_gene_id\"\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mstr\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msplit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m\",\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    170\u001B[0m     \u001B[0mconversion\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mconversion\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mexplode\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m\"ensembl_gene_id\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    171\u001B[0m     \u001B[0mconversion\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mreset_index\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0minplace\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mTrue\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdrop\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mTrue\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    172\u001B[0m     \u001B[0;31m# print(conversion)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 173\u001B[0;31m     \u001B[0mmatrix\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mmatrix\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mmerge\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mconversion\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mon\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m\"ensembl_gene_id\"\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m\"entrez_gene_id\"\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m\"gene_symbol\"\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mhow\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m\"left\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    174\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    175\u001B[0m     \u001B[0;31m# Only include Entrez and Ensembl Gene IDs that are present in `gene_info`\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    176\u001B[0m     \u001B[0mmatrix\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m\"entrez_gene_id\"\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mmatrix\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m\"entrez_gene_id\"\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mstr\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msplit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m\"//\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
+      "\u001B[0;32m~/Projects/COMO/.venv/lib/python3.12/site-packages/pandas/core/frame.py\u001B[0m in \u001B[0;36m?\u001B[0;34m(self, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001B[0m\n\u001B[1;32m  10828\u001B[0m         \u001B[0mvalidate\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mMergeValidate\u001B[0m \u001B[0;34m|\u001B[0m \u001B[0;32mNone\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m  10829\u001B[0m     \u001B[0;34m)\u001B[0m \u001B[0;34m->\u001B[0m \u001B[0mDataFrame\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m  10830\u001B[0m         \u001B[0;32mfrom\u001B[0m \u001B[0mpandas\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcore\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mreshape\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mmerge\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mmerge\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m  10831\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m> 10832\u001B[0;31m         return merge(\n\u001B[0m\u001B[1;32m  10833\u001B[0m             \u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m  10834\u001B[0m             \u001B[0mright\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m  10835\u001B[0m             \u001B[0mhow\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mhow\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
+      "\u001B[0;32m~/Projects/COMO/.venv/lib/python3.12/site-packages/pandas/core/reshape/merge.py\u001B[0m in \u001B[0;36m?\u001B[0;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001B[0m\n\u001B[1;32m    166\u001B[0m             \u001B[0mvalidate\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mvalidate\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    167\u001B[0m             \u001B[0mcopy\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mcopy\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    168\u001B[0m         \u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    169\u001B[0m     \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 170\u001B[0;31m         op = _MergeOperation(\n\u001B[0m\u001B[1;32m    171\u001B[0m             \u001B[0mleft_df\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    172\u001B[0m             \u001B[0mright_df\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    173\u001B[0m             \u001B[0mhow\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mhow\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
+      "\u001B[0;32m~/Projects/COMO/.venv/lib/python3.12/site-packages/pandas/core/reshape/merge.py\u001B[0m in \u001B[0;36m?\u001B[0;34m(self, left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, indicator, validate)\u001B[0m\n\u001B[1;32m    790\u001B[0m             \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mright_join_keys\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    791\u001B[0m             \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mjoin_names\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    792\u001B[0m             \u001B[0mleft_drop\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    793\u001B[0m             \u001B[0mright_drop\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 794\u001B[0;31m         \u001B[0;34m)\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_get_merge_keys\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    795\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    796\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mleft_drop\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    797\u001B[0m             \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mleft\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mleft\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_drop_labels_or_levels\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mleft_drop\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
+      "\u001B[0;32m~/Projects/COMO/.venv/lib/python3.12/site-packages/pandas/core/reshape/merge.py\u001B[0m in \u001B[0;36m?\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m   1306\u001B[0m                     \u001B[0;32mif\u001B[0m \u001B[0mlk\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0;32mNone\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1307\u001B[0m                         \u001B[0;31m# Then we're either Hashable or a wrong-length arraylike,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1308\u001B[0m                         \u001B[0;31m#  the latter of which will raise\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1309\u001B[0m                         \u001B[0mlk\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mcast\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mHashable\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mlk\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1310\u001B[0;31m                         \u001B[0mleft_keys\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mappend\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mleft\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_get_label_or_level_values\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mlk\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   1311\u001B[0m                         \u001B[0mjoin_names\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mappend\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mlk\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1312\u001B[0m                     \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1313\u001B[0m                         \u001B[0;31m# work-around for merge_asof(left_index=True)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
+      "\u001B[0;32m~/Projects/COMO/.venv/lib/python3.12/site-packages/pandas/core/generic.py\u001B[0m in \u001B[0;36m?\u001B[0;34m(self, key, axis)\u001B[0m\n\u001B[1;32m   1907\u001B[0m             \u001B[0mvalues\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mxs\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mkey\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0maxis\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mother_axes\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_values\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1908\u001B[0m         \u001B[0;32melif\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_is_level_reference\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mkey\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0maxis\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0maxis\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1909\u001B[0m             \u001B[0mvalues\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0maxes\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0maxis\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mget_level_values\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mkey\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_values\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1910\u001B[0m         \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 1911\u001B[0;31m             \u001B[0;32mraise\u001B[0m \u001B[0mKeyError\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mkey\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m   1912\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1913\u001B[0m         \u001B[0;31m# Check for duplicates\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   1914\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mvalues\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mndim\u001B[0m \u001B[0;34m>\u001B[0m \u001B[0;36m1\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
+      "\u001B[0;31mKeyError\u001B[0m: 'entrez_gene_id'"
+     ]
+    }
+   ],
+   "execution_count": 3
   },
   {
    "cell_type": "markdown",
@@ -707,14 +825,12 @@
     "merge_zfpkm_distrubution = True\n",
     "keep_transcriptomics_score = True\n",
     "\n",
-    "\n",
     "mrna_batches = {\"naiveB\": [\n",
     "    \"naiveB_S1R1\", \"naiveB_S1R2\", \"naiveB_S1R3\", \"naiveB_S1R4\",\n",
     "    \"naiveB_S2R1\", \"naiveB_S2R2\", \"naiveB_S2R3\", \"naiveB_S2R4\",\n",
     "]}\n",
     "trna_batches = {\"naiveB\": [\"naiveB_S3R1\", \"naiveB_S3R2\", \"naiveB_S3R3\"]}\n",
     "\n",
-    "\n",
     "for context in context_names:\n",
     "    await merge_xomics(\n",
     "        context_name=context,\n",
@@ -1011,7 +1127,6 @@
     "data_source = \"rnaseq\"\n",
     "taxon_id = \"human\"\n",
     "\n",
-    "\n",
     "for context_name in stringlist_to_list(context_names):\n",
     "    disease_config_file = f\"disease_data_inputs_{context_name}.xlsx\"\n",
     "\n",
@@ -1083,7 +1198,6 @@
     "}\n",
     "sovler = \"gurobi\"\n",
     "\n",
-    "\n",
     "config = Config()\n",
     "\n",
     "drug_raw_file = \"Repurposing_Hub_export.txt\"\n",
@@ -1111,7 +1225,7 @@
     "                tissue_specific_model_filepath = model_files[context]\n",
     "            else:\n",
     "                tissue_specific_model_filepath = (\n",
-    "                    config.data_dir / \"results\" / context / f\"{context}_SpecificModel_{recon_algorithm}.mat\"\n",
+    "                        config.data_dir / \"results\" / context / f\"{context}_SpecificModel_{recon_algorithm}.mat\"\n",
     "                )\n",
     "\n",
     "            # fmt: off\n",
@@ -1152,7 +1266,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.15"
+   "version": "3.12.6"
   },
   "toc-autonumbering": true,
   "toc-showcode": true

From a3c4b2883c1bed8d51c15533a1ad7e80dba1e7d1 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 3 Jun 2025 16:12:48 -0500
Subject: [PATCH 290/345] style: ruff formatting; error handling

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/utils.py | 47 +++++++++++++++++++---------------------------
 1 file changed, 19 insertions(+), 28 deletions(-)

diff --git a/main/como/utils.py b/main/como/utils.py
index 22652f6a..fc8c9739 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -47,20 +47,14 @@ def stringlist_to_list(stringlist: str | list[str]) -> list[str]:
     new_list: list[str] = stringlist.strip("[]").replace("'", "").replace(" ", "").split(",")
 
     # Show a warning if more than one item is present in the list (this means we are using the old method)
-    logger.critical(
-        "DeprecationWarning: Please use the new method of providing context names, "
-        "i.e. --output-filetypes 'type1 type2 type3'."
-    )
+    logger.critical("DeprecationWarning: Please use the new method of providing context names, i.e. --output-filetypes 'type1 type2 type3'.")
     logger.critical(
         "If you are using COMO, this can be done by setting the 'context_names' variable to a "
         "simple string separated by spaces. Here are a few examples!"
     )
     logger.critical("context_names = 'cellType1 cellType2 cellType3'")
     logger.critical("output_filetypes = 'output1 output2 output3'")
-    logger.critical(
-        "\nYour current method of passing context names will be removed in the future. "
-        "Update your variables above accordingly!\n\n"
-    )
+    logger.critical("\nYour current method of passing context names will be removed in the future. Update your variables above accordingly!\n\n")
 
     return new_list
 
@@ -84,9 +78,9 @@ def split_gene_expression_data(
     expression_data = expression_data[["entrez_gene_id", "active"]]
     single_gene_names = expression_data[~expression_data["entrez_gene_id"].astype(str).str.contains("//")]
     multiple_gene_names = expression_data[expression_data["entrez_gene_id"].astype(str).str.contains("//")]
-    split_gene_names = multiple_gene_names.assign(
-        entrez_gene_id=multiple_gene_names["entrez_gene_id"].astype(str).str.split("///")
-    ).explode("entrez_gene_id")
+    split_gene_names = multiple_gene_names.assign(entrez_gene_id=multiple_gene_names["entrez_gene_id"].astype(str).str.split("///")).explode(
+        "entrez_gene_id"
+    )
 
     gene_expressions = pd.concat([single_gene_names, split_gene_names], axis=0, ignore_index=True)
     if entrez_as_index:
@@ -120,9 +114,7 @@ async def _format_determination(
     :return: A pandas DataFrame
     """
     requested_output = [requested_output] if isinstance(requested_output, Output) else requested_output
-    coercion = (await biodbnet.db_find(values=input_values, output_db=requested_output, taxon=taxon)).drop(
-        columns=["Input Type"]
-    )
+    coercion = (await biodbnet.db_find(values=input_values, output_db=requested_output, taxon=taxon)).drop(columns=["Input Type"])
     coercion.columns = pd.Index(["input_value", *[o.value.replace(" ", "_").lower() for o in requested_output]])
     return coercion
 
@@ -147,16 +139,16 @@ async def _read_file(
         return None
 
     if isinstance(path, Path) and not path.exists():
-        logger.critical(f"File {path} does not exist")
-        raise FileNotFoundError(f"File {path} does not exist")
+        _log_and_raise_error(f"File {path} does not exist", error=FileNotFoundError, level=LogLevel.CRITICAL)
 
     # StringIO is used if a CSV file is read using open() directly
     if isinstance(path, io.StringIO):
         return pd.read_csv(path, **kwargs)
 
     match path.suffix:
-        case ".csv" | ".tsv":
-            kwargs.setdefault("sep", "," if path.suffix == ".csv" else "\t")
+        case ".csv" | ".tsv" | ".txt":
+            if "sep" not in kwargs:
+                kwargs.setdefault("sep", "," if path.suffix == ".csv" else "\t")
             with path.open("r") as i_stream:
                 content = i_stream.read()
                 return pd.read_csv(io.StringIO(content), **kwargs)
@@ -171,13 +163,12 @@ async def _read_file(
                 return df
             return adata
         case _:
-            logger.critical(
-                f"Unknown file extension '{path.suffix}'. Valid options are '.tsv', '.csv', '.xlsx', '.xls', or '.h5ad'"
-            )
-            raise ValueError(
-                f"Unknown file extension '{path.suffix}'. "
-                f"Valid options are '.tsv', '.csv', '.xlsx', '.xls', or '.h5ad'."
+            _log_and_raise_error(
+                f"Unknown file extension '{path.suffix}'. Valid options are '.tsv', '.csv', '.xlsx', '.xls', or '.h5ad'",
+                error=ValueError,
+                level=LogLevel.CRITICAL,
             )
+            return None
 
 
 async def get_missing_gene_data(values: list[str] | pd.DataFrame, taxon_id: int | str | Taxon) -> pd.DataFrame:
@@ -202,9 +193,7 @@ async def get_missing_gene_data(values: list[str] | pd.DataFrame, taxon_id: int
             return await get_missing_gene_data(values["ensembl_gene_id"].tolist(), taxon_id=taxon_id)
         else:
             logger.critical("Unable to find 'gene_symbol', 'entrez_gene_id', or 'ensembl_gene_id' in the input matrix.")
-            raise ValueError(
-                "Unable to find 'gene_symbol', 'entrez_gene_id', or 'ensembl_gene_id' in the input matrix."
-            )
+            raise ValueError("Unable to find 'gene_symbol', 'entrez_gene_id', or 'ensembl_gene_id' in the input matrix.")
 
 
 def _listify(value):
@@ -225,10 +214,12 @@ def return_placeholder_data() -> pd.DataFrame:
 
 
 def _set_up_logging(
-    level: LogLevel,
+    level: LogLevel | str,
     location: str | TextIOWrapper,
     formatting: str = LOG_FORMAT,
 ):
+    if isinstance(level, str):
+        level = LogLevel[level.upper()]
     with contextlib.suppress(ValueError):
         logger.remove(0)
         logger.add(sink=location, level=level.value, format=formatting)

From c0f5f00c5abe531eeef412c6574d2856dda33caf Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 3 Jun 2025 16:17:04 -0500
Subject: [PATCH 291/345] refactor: remove unwrapping aio file reading for
 better consistency/reproducibility

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 23 +++++++++++++----------
 1 file changed, 13 insertions(+), 10 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 40e8a01d..6375e06a 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -4,12 +4,13 @@
 import json
 import re
 import sys
-from dataclasses import dataclass, field
+from dataclasses import asdict, dataclass, field
 from io import StringIO, TextIOWrapper
 from itertools import chain
 from pathlib import Path
-from typing import Literal
+from typing import Final, Literal
 
+import aiofiles
 import numpy as np
 import pandas as pd
 from fast_bioservices.biothings.mygene import MyGene
@@ -41,16 +42,18 @@ async def build_from_tab(cls, filepath: Path) -> _STARinformation:
                 error=ValueError,
                 level=LogLevel.ERROR,
             )
+        if not filepath.exists():
+            _log_and_raise_error(
+                f"Unable to find the .tab file '{filepath}'",
+                error=FileNotFoundError,
+                level=LogLevel.ERROR,
+            )
 
         with filepath.open("r") as i_stream:
-            unmapped, multimapping, no_feature, ambiguous = await asyncio.gather(
-                *[
-                    asyncio.to_thread(i_stream.readline),
-                    asyncio.to_thread(i_stream.readline),
-                    asyncio.to_thread(i_stream.readline),
-                    asyncio.to_thread(i_stream.readline),
-                ]
-            )
+            unmapped = i_stream.readline()
+            multimapping = i_stream.readline()
+            no_feature = i_stream.readline()
+            ambiguous = i_stream.readline()
 
             num_unmapped = [int(i) for i in unmapped.rstrip("\n").split("\t")[1:]]
             num_multimapping = [int(i) for i in multimapping.rstrip("\n").split("\t")[1:]]

From f1a41370fa906825438060572a678eb126fbc294 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 3 Jun 2025 16:19:42 -0500
Subject: [PATCH 292/345] feat: add dataclasses to handle types more easily

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 473 ++++++++++++++++++++-------------
 1 file changed, 281 insertions(+), 192 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 6375e06a..e4c7c416 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -141,10 +141,41 @@ def __post_init__(self):
         self.__sample_names.sort()
 
 
+@dataclass(slots=True)
+class SampleConfiguration:
+    sample_name: str
+    fragment_length: float
+    layout: str
+    strand: str
+    study: str
+    library_prep: str
+
+
+async def _read_text(path: Path | None, *, default: str, lower: bool = False) -> str:
+    if path is None:
+        return default
+    async with aiofiles.open(path) as f:
+        txt = (await f.read()).strip()
+    return txt.lower() if lower else txt
+
+
 def _sample_name_from_filepath(file: Path) -> str:
     return re.search(r".+_S\d+R\d+", file.stem).group()
 
 
+def _require_one(paths: list[Path], kind: Literal["layout", "strand", "preparation", "fragment"], label: str) -> Path | None:
+    if len(paths) == 1:
+        return paths[0]
+    if len(paths) == 0:
+        return None
+    _log_and_raise_error(
+        f"Multiple matching {kind} files for {label}, make sure there is only one copy for each replicate in COMO_input",
+        error=ValueError,
+        level=LogLevel.ERROR,
+    )
+    return None  # explicit return None to satisfy type-check
+
+
 def _organize_gene_counts_files(data_dir: Path) -> list[_StudyMetrics]:
     gene_count_dir = Path(data_dir, "geneCounts")
     strand_dir = Path(data_dir, "strandedness")
@@ -156,7 +187,7 @@ def _organize_gene_counts_files(data_dir: Path) -> list[_StudyMetrics]:
         _log_and_raise_error(
             (
                 f"Unequal number of gene count directories and strandedness directories. "
-                f"Found {len(gene_counts_directories)} gene count directories and {len(strandedness_directories)} strandedness directories."  # noqa: E501
+                f"Found {len(gene_counts_directories)} gene count directories and {len(strandedness_directories)} strandedness directories."
                 f"\nGene count directory: {gene_count_dir}\nStrandedness directory: {strand_dir}"
             ),
             error=ValueError,
@@ -210,11 +241,7 @@ async def _process_first_multirun_sample(strand_file: Path, all_counts_files: li
 
         run_counts = star_information.count_matrix[["ensembl_gene_id", strand_information]]
         run_counts.columns = pd.Index(["ensembl_gene_id", "counts"])
-        sample_count = (
-            run_counts
-            if sample_count.empty
-            else sample_count.merge(run_counts, on=["ensembl_gene_id", "counts"], how="outer")
-        )
+        sample_count = run_counts if sample_count.empty else sample_count.merge(run_counts, on=["ensembl_gene_id", "counts"], how="outer")
 
     # Set na values to 0
     sample_count = sample_count.fillna(value="0")
@@ -336,182 +363,253 @@ async def _create_config_df(  # noqa: C901
     The configuration file created is based on the gene counts matrix.
      If using zFPKM normalization technique, mean fragment lengths will be fetched
     """
-    gene_counts_dir = como_context_dir / gene_count_dirname
-    layout_dir = como_context_dir / layout_dirname
-    strandedness_dir = como_context_dir / strandedness_dirname
-    fragment_sizes_dir = como_context_dir / fragment_sizes_dirname
-    prep_method_dir = como_context_dir / prep_method_dirname
-
-    gene_counts_files = list(gene_counts_dir.rglob("*.tab"))
-    sample_names: list[str] = []
-    fragment_lengths: list[int | float] = []
-    layouts: list[str] = []
-    strands: list[str] = []
-    groups: list[str] = []
-    preparation_method: list[str] = []
-
-    if len(gene_counts_files) == 0:
+    label_regex: Final = re.compile(r"(?P<study>S\d{1,3})(?P<rep>R\d{1,3})(?:(?P<run>r\d{1,3}))?")
+    gene_counts: list[Path] = list((como_context_dir / gene_count_dirname).rglob("*.tab"))
+    if not gene_counts:
         _log_and_raise_error(
-            f"No gene count files found in '{gene_counts_dir}'.", error=FileNotFoundError, level=LogLevel.ERROR
+            f"No gene count files found in '{gene_count_dirname}'",
+            error=FileNotFoundError,
+            level=LogLevel.ERROR,
         )
 
-    for gene_count_filename in sorted(gene_counts_files):
-        # Match S___R___r___
-        # \d{1,3} matches 1-3 digits
-        # (?:r\d{1,3})? optionally matches a "r" followed by three digits
-        label = re.findall(r"S\d{1,3}R\d{1,3}(?:r\d{1,3})?", gene_count_filename.as_posix())[0]
-        if not label:
+    auxillary_directories = {
+        "layout": como_context_dir / layout_dirname,
+        "strand": como_context_dir / strandedness_dirname,
+        "fragment": como_context_dir / fragment_sizes_dirname,
+        "prep": como_context_dir / prep_method_dirname,
+    }
+    aux_lookup: dict[str, dict[str, Path]] = {kind: {} for kind in auxillary_directories}
+    for kind, root in auxillary_directories.items():
+        kind: str
+        root: Path
+        for p in root.rglob("*"):
+            if p.is_file():
+                m = label_regex.search(p.stem)
+                if m:
+                    aux_lookup[kind][m.group(0)] = p
+
+    rows: list[SampleConfiguration] = []
+    for gene_count_path in sorted(gene_counts):
+        m = label_regex.search(gene_count_path.as_posix())
+        if not m:
             _log_and_raise_error(
-                (
-                    f"\n\nFilename of '{gene_count_filename}' is not valid. "
-                    f"Should be 'contextName_SXRYrZ.tab', "
-                    f"where X is the study/batch number, Y is the replicate number, "
-                    f"and Z is the run number."
-                    "\n\nIf not a multi-run sample, exclude 'rZ' from the filename."
-                ),
+                f"Filename '{gene_count_path.name}' does not match contextName_SXRYrZ.tab pattern",
                 error=ValueError,
                 level=LogLevel.ERROR,
             )
-
-        study_number = re.findall(r"S\d{1,3}", label)[0]
-        rep_number = re.findall(r"R\d{1,3}", label)[0]
-        run_number = re.findall(r"r\d{1,3}", label)
-
-        multi_flag = 0
-        if len(run_number) > 0:
-            if run_number[0] != "r1":
-                continue
-            label_glob = f"{study_number}{rep_number}r*"  # S__R__r*
-            runs = [run for run in gene_counts_files if re.search(label_glob, run.as_posix())]
-            multi_flag = 1
-            frag_files = []
-
-            for run in runs:
-                run_number = re.findall(r"R\d{1,3}", run.as_posix())[0]
-                replicate = re.findall(r"r\d{1,3}", run.as_posix())[0]
-                frag_filename = "".join([context_name, "_", study_number, run_number, replicate, "_fragment_size.txt"])
-                frag_files.append(como_context_dir / fragment_sizes_dirname / study_number / frag_filename)
-
-        layout_files: list[Path] = list(layout_dir.rglob(f"{context_name}_{label}_layout.txt"))
-        strand_files: list[Path] = list(strandedness_dir.rglob(f"{context_name}_{label}_strandedness.txt"))
-        frag_files: list[Path] = list(fragment_sizes_dir.rglob(f"{context_name}_{label}_fragment_size.txt"))
-        prep_files: list[Path] = list(prep_method_dir.rglob(f"{context_name}_{label}_prep_method.txt"))
-
-        layout = "UNKNOWN"
-        if len(layout_files) == 0:
-            logger.warning(
-                f"No layout file found for {label}, writing as 'UNKNOWN', "
-                f"this should be defined if you are using zFPKM or downstream 'rnaseq_gen.py' will not run"
-            )
-        elif len(layout_files) == 1:
-            with layout_files[0].open("r") as file:
-                layout = file.read().strip()
-        elif len(layout_files) > 1:
-            _log_and_raise_error(
-                (
-                    f"Multiple matching layout files for {label}, "
-                    f"make sure there is only one copy for each replicate in COMO_input"
-                ),
-                error=ValueError,
-                level=LogLevel.ERROR,
-            )
-
-        strand = "UNKNOWN"
-        if len(strand_files) == 0:
-            logger.warning(
-                f"No strandedness file found for {label}, writing as 'UNKNOWN'. "
-                f"This will not interfere with the analysis since you have already set rnaseq_preprocess.py to "
-                f"infer the strandedness when writing the counts matrix"
-            )
-        elif len(strand_files) == 1:
-            with strand_files[0].open("r") as file:
-                strand = file.read().strip()
-        elif len(strand_files) > 1:
-            _log_and_raise_error(
-                (
-                    f"Multiple matching strandedness files for {label}, "
-                    f"make sure there is only one copy for each replicate in COMO_input"
-                ),
-                error=ValueError,
-                level=LogLevel.ERROR,
-            )
-
-        prep = "total"
-        if len(prep_files) == 0:
-            logger.warning(f"No prep file found for {label}, assuming 'total', as in 'Total RNA' library preparation")
-        elif len(prep_files) == 1:
-            with prep_files[0].open("r") as file:
-                prep = file.read().strip().lower()
-                if prep not in ["total", "mrna"]:
-                    _log_and_raise_error(
-                        f"Prep method must be either 'total' or 'mrna' for {label}",
-                        error=ValueError,
-                        level=LogLevel.ERROR,
-                    )
-        elif len(prep_files) > 1:
+        label = m.group(0)
+        study_number = m["study"]
+        rep_number = m["rep"]
+        sample_id = f"{context_name}_{study_number}{rep_number}"
+
+        layout_path = _require_one([aux_lookup["layout"].get(label)], "layout", label)
+        strand_path = _require_one([aux_lookup["strand"].get(label)], "strand", label)
+        prep_path = _require_one([aux_lookup["prep"].get(label)], "preparation", label)
+
+        layout, strand, prep = await asyncio.gather(
+            *[
+                _read_text(layout_path, default="UNKNOWN"),
+                _read_text(strand_path, default="UNKNOWN"),
+                _read_text(prep_path, default="total", lower=True),
+            ],
+        )
+        if prep not in {"total", "mrna"}:
             _log_and_raise_error(
-                (
-                    f"Multiple matching prep files for {label}, "
-                    f"make sure there is only one copy for each replicate in COMO_input"
-                ),
+                f"Prep method must be 'total' or 'mrna' (got '{prep}') for {label}",
                 error=ValueError,
                 level=LogLevel.ERROR,
             )
 
-        mean_fragment_size = 100
-        if len(frag_files) == 0 and prep != RNAType.TRNA.value:
-            logger.warning(
-                f"No fragment file found for {label}, using '100'. "
-                "You should define this if you are going to use downstream zFPKM normalization"
-            )
-        elif len(frag_files) == 1:
-            if layout == "single-end":
-                mean_fragment_size = 0
-            else:
-                if not multi_flag:
-                    frag_df = pd.read_table(frag_files[0], low_memory=False)
-                    frag_df["meanxcount"] = frag_df["frag_mean"] * frag_df["frag_count"]
-                    mean_fragment_size = sum(frag_df["meanxcount"] / sum(frag_df["frag_count"]))
-
-                else:
-                    mean_fragment_sizes = np.array([])
-                    library_sizes = np.array([])
-                    for ff in frag_files:
-                        frag_df = pd.read_table(ff, low_memory=False, sep="\t", on_bad_lines="skip")
-                        frag_df["meanxcount"] = frag_df["frag_mean"] * frag_df["frag_count"]
-                        mean_fragment_size = sum(frag_df["meanxcount"] / sum(frag_df["frag_count"]))
-                        mean_fragment_sizes = np.append(mean_fragment_sizes, mean_fragment_size)
-                        library_sizes = np.append(library_sizes, sum(frag_df["frag_count"]))
-
-                    mean_fragment_size = sum(mean_fragment_sizes * library_sizes) / sum(library_sizes)
-        elif len(frag_files) > 1:
-            _log_and_raise_error(
-                (
-                    f"Multiple matching fragment files for {label}, "
-                    f"make sure there is only one copy for each replicate in COMO_input"
-                ),
-                error=ValueError,
-                level=LogLevel.ERROR,
+        fragment_label = f"{context_name}_{label}_fragment_size.txt"
+        frag_paths = [p for p in aux_lookup["fragment"].values() if p.name == fragment_label]
+        if not frag_paths and prep != RNAType.TRNA.value:
+            logger.warning(f"No fragment file for '{label}'; defaulting to 100 bp (needed for zFPKM).")
+            mean_frag = 100.0
+        elif len(frag_paths) == 1 and layout == "single-end":
+            mean_frag = 0.0
+        else:  # 1-N files, paired end
+            dfs: list[pd.DataFrame] = await asyncio.gather(*[_read_file(f, sep="\t", on_bad_lines="skip") for f in frag_paths])
+            for df in dfs:
+                df["meanxcount"] = df["frag_mean"] * df["frag_count"]
+                counts = np.array([df["frag_count"].sum() for df in dfs])
+                means = np.array([(df["meanxcount"] / df["frag_count"].sum()).sum() for df in dfs])
+                mean_frag = float(np.average(means, weights=counts))
+        rows.append(
+            SampleConfiguration(
+                sample_name=sample_id,
+                fragment_length=mean_frag,
+                layout=layout,
+                strand=strand,
+                study=study_number,
+                library_prep=prep,
             )
+        )
 
-        sample_names.append(f"{context_name}_{study_number}{rep_number}")
-        fragment_lengths.append(mean_fragment_size)
-        layouts.append(layout)
-        strands.append(strand)
-        groups.append(study_number)
-        preparation_method.append(prep)
-
-    out_df = pd.DataFrame(
-        {
-            "sample_name": sample_names,
-            "fragment_length": fragment_lengths,
-            "layout": layouts,
-            "strand": strands,
-            "study": groups,
-            "library_prep": preparation_method,
-        }
-    ).sort_values("sample_name")
-    return out_df
+    df = pd.DataFrame.from_records([asdict(r) for r in rows]).sort_values("sample_name", ignore_index=True)
+    return df
+
+    # 6-3-25: Intentionally left commented-out code to test its replacement
+    # gene_counts_dir = como_context_dir / gene_count_dirname
+    # layout_dir = como_context_dir / layout_dirname
+    # strandedness_dir = como_context_dir / strandedness_dirname
+    # fragment_sizes_dir = como_context_dir / fragment_sizes_dirname
+    # prep_method_dir = como_context_dir / prep_method_dirname
+    #
+    # gene_counts_files = list(gene_counts_dir.rglob("*.tab"))
+    # sample_names: list[str] = []
+    # fragment_lengths: list[int | float] = []
+    # layouts: list[str] = []
+    # strands: list[str] = []
+    # groups: list[str] = []
+    # preparation_method: list[str] = []
+    #
+    # if len(gene_counts_files) == 0:
+    #     _log_and_raise_error(f"No gene count files found in '{gene_counts_dir}'.", error=FileNotFoundError, level=LogLevel.ERROR)
+    #
+    # for gene_count_filename in sorted(gene_counts_files):
+    #     # Match S___R___r___
+    #     # \d{1,3} matches 1-3 digits
+    #     # (?:r\d{1,3})? optionally matches a "r" followed by three digits
+    #     label = re.findall(r"S\d{1,3}R\d{1,3}(?:r\d{1,3})?", gene_count_filename.as_posix())[0]
+    #     if not label:
+    #         _log_and_raise_error(
+    #             (
+    #                 f"\n\nFilename of '{gene_count_filename}' is not valid. "
+    #                 f"Should be 'contextName_SXRYrZ.tab', "
+    #                 f"where X is the study/batch number, Y is the replicate number, "
+    #                 f"and Z is the run number."
+    #                 "\n\nIf not a multi-run sample, exclude 'rZ' from the filename."
+    #             ),
+    #             error=ValueError,
+    #             level=LogLevel.ERROR,
+    #         )
+    #
+    #     study_number = re.findall(r"S\d{1,3}", label)[0]
+    #     rep_number = re.findall(r"R\d{1,3}", label)[0]
+    #     run_number = re.findall(r"r\d{1,3}", label)
+    #
+    #     multi_flag = 0
+    #     if len(run_number) > 0:
+    #         if run_number[0] != "r1":
+    #             continue
+    #         label_glob = f"{study_number}{rep_number}r*"  # S__R__r*
+    #         runs = [run for run in gene_counts_files if re.search(label_glob, run.as_posix())]
+    #         multi_flag = 1
+    #         frag_files = []
+    #
+    #         for run in runs:
+    #             run_number = re.findall(r"R\d{1,3}", run.as_posix())[0]
+    #             replicate = re.findall(r"r\d{1,3}", run.as_posix())[0]
+    #             frag_filename = "".join([context_name, "_", study_number, run_number, replicate, "_fragment_size.txt"])
+    #             frag_files.append(como_context_dir / fragment_sizes_dirname / study_number / frag_filename)
+    #
+    #     layout_files: list[Path] = list(layout_dir.rglob(f"{context_name}_{label}_layout.txt"))
+    #     strand_files: list[Path] = list(strandedness_dir.rglob(f"{context_name}_{label}_strandedness.txt"))
+    #     frag_files: list[Path] = list(fragment_sizes_dir.rglob(f"{context_name}_{label}_fragment_size.txt"))
+    #     prep_files: list[Path] = list(prep_method_dir.rglob(f"{context_name}_{label}_prep_method.txt"))
+    #
+    #     layout = "UNKNOWN"
+    #     if len(layout_files) == 0:
+    #         logger.warning(
+    #             f"No layout file found for {label}, writing as 'UNKNOWN', "
+    #             f"this should be defined if you are using zFPKM or downstream 'rnaseq_gen.py' will not run"
+    #         )
+    #     elif len(layout_files) == 1:
+    #         with layout_files[0].open("r") as file:
+    #             layout = file.read().strip()
+    #     elif len(layout_files) > 1:
+    #         _log_and_raise_error(
+    #             f"Multiple matching layout files for {label}, make sure there is only one copy for each replicate in COMO_input",
+    #             error=ValueError,
+    #             level=LogLevel.ERROR,
+    #         )
+    #
+    #     strand = "UNKNOWN"
+    #     if len(strand_files) == 0:
+    #         logger.warning(
+    #             f"No strandedness file found for {label}, writing as 'UNKNOWN'. "
+    #             f"This will not interfere with the analysis since you have already set rnaseq_preprocess.py to "
+    #             f"infer the strandedness when writing the counts matrix"
+    #         )
+    #     elif len(strand_files) == 1:
+    #         with strand_files[0].open("r") as file:
+    #             strand = file.read().strip()
+    #     elif len(strand_files) > 1:
+    #         _log_and_raise_error(
+    #             f"Multiple matching strandedness files for {label}, make sure there is only one copy for each replicate in COMO_input",
+    #             error=ValueError,
+    #             level=LogLevel.ERROR,
+    #         )
+    #
+    #     prep = "total"
+    #     if len(prep_files) == 0:
+    #         logger.warning(f"No prep file found for {label}, assuming 'total', as in 'Total RNA' library preparation")
+    #     elif len(prep_files) == 1:
+    #         with prep_files[0].open("r") as file:
+    #             prep = file.read().strip().lower()
+    #             if prep not in ["total", "mrna"]:
+    #                 _log_and_raise_error(
+    #                     f"Prep method must be either 'total' or 'mrna' for {label}",
+    #                     error=ValueError,
+    #                     level=LogLevel.ERROR,
+    #                 )
+    #     elif len(prep_files) > 1:
+    #         _log_and_raise_error(
+    #             f"Multiple matching prep files for {label}, make sure there is only one copy for each replicate in COMO_input",
+    #             error=ValueError,
+    #             level=LogLevel.ERROR,
+    #         )
+    #
+    #     mean_fragment_size = 100
+    #     if len(frag_files) == 0 and prep != RNAType.TRNA.value:
+    #         logger.warning(
+    #             f"No fragment file found for {label}, using '100'. You should define this if you are going to use downstream zFPKM normalization"
+    #         )
+    #     elif len(frag_files) == 1:
+    #         if layout == "single-end":
+    #             mean_fragment_size = 0
+    #         else:
+    #             if not multi_flag:
+    #                 frag_df = pd.read_table(frag_files[0], low_memory=False)
+    #                 frag_df["meanxcount"] = frag_df["frag_mean"] * frag_df["frag_count"]
+    #                 mean_fragment_size = sum(frag_df["meanxcount"] / sum(frag_df["frag_count"]))
+    #
+    #             else:
+    #                 mean_fragment_sizes = np.array([])
+    #                 library_sizes = np.array([])
+    #                 for ff in frag_files:
+    #                     frag_df = pd.read_table(ff, low_memory=False, sep="\t", on_bad_lines="skip")
+    #                     frag_df["meanxcount"] = frag_df["frag_mean"] * frag_df["frag_count"]
+    #                     mean_fragment_size = sum(frag_df["meanxcount"] / sum(frag_df["frag_count"]))
+    #                     mean_fragment_sizes = np.append(mean_fragment_sizes, mean_fragment_size)
+    #                     library_sizes = np.append(library_sizes, sum(frag_df["frag_count"]))
+    #
+    #                 mean_fragment_size = sum(mean_fragment_sizes * library_sizes) / sum(library_sizes)
+    #     elif len(frag_files) > 1:
+    #         _log_and_raise_error(
+    #             f"Multiple matching fragment files for {label}, make sure there is only one copy for each replicate in COMO_input",
+    #             error=ValueError,
+    #             level=LogLevel.ERROR,
+    #         )
+    #
+    #     sample_names.append(f"{context_name}_{study_number}{rep_number}")
+    #     fragment_lengths.append(mean_fragment_size)
+    #     layouts.append(layout)
+    #     strands.append(strand)
+    #     groups.append(study_number)
+    #     preparation_method.append(prep)
+    #
+    # out_df = pd.DataFrame(
+    #     {
+    #         "sample_name": sample_names,
+    #         "fragment_length": fragment_lengths,
+    #         "layout": layouts,
+    #         "strand": strands,
+    #         "study": groups,
+    #         "library_prep": preparation_method,
+    #     }
+    # ).sort_values("sample_name")
+    # return out_df
 
 
 async def _create_gene_info_file(
@@ -584,7 +682,7 @@ async def read_counts(file: Path) -> list[str]:
     logger.success(f"Gene Info file written at '{output_filepath}'")
 
 
-async def _create_matrix_file(
+async def _process_como_input(
     context_name: str,
     output_config_filepath: Path,
     como_context_dir: PATH_TYPE,
@@ -626,7 +724,7 @@ async def _process(
     for rna, output_config_filepath, output_matrix_filepath in rna_types:
         tasks.append(
             asyncio.create_task(
-                _create_matrix_file(
+                _process_como_input(
                     context_name=context_name,
                     output_config_filepath=output_config_filepath,
                     como_context_dir=como_context_dir,
@@ -639,12 +737,15 @@ async def _process(
     await asyncio.gather(*tasks)
 
     # create the gene info filepath based on provided data
+
+    input_files = []
+    if input_matrix_filepath:
+        input_files.extend(input_matrix_filepath)
+    output_trna_matrix_filepath and input_files.append(output_trna_matrix_filepath)
+    output_mrna_matrix_filepath and input_files.append(output_mrna_matrix_filepath)
+
     await _create_gene_info_file(
-        counts_matrix_filepaths=[
-            f
-            for f in [*input_matrix_filepath, output_trna_matrix_filepath, output_mrna_matrix_filepath]
-            if f is not None
-        ],
+        counts_matrix_filepaths=input_files,
         output_filepath=output_gene_info_filepath,
         taxon=taxon,
         cache=cache,
@@ -662,7 +763,7 @@ async def rnaseq_preprocess(
     output_trna_count_matrix_filepath: Path | None = None,
     output_mrna_count_matrix_filepath: Path | None = None,
     cache: bool = True,
-    log_level: LogLevel = LogLevel.INFO,
+    log_level: LogLevel | str = LogLevel.INFO,
     log_location: str | TextIOWrapper = sys.stderr,
 ) -> None:
     """Preprocesses RNA-seq data for downstream analysis.
@@ -691,22 +792,10 @@ async def rnaseq_preprocess(
     if como_context_dir:
         como_context_dir = como_context_dir.resolve()
     input_matrix_filepath = [i.resolve() for i in _listify(input_matrix_filepath)] if input_matrix_filepath else None
-    output_trna_metadata_filepath = (
-        output_trna_metadata_filepath.resolve() if output_trna_metadata_filepath else output_trna_metadata_filepath
-    )
-    output_mrna_metadata_filepath = (
-        output_mrna_metadata_filepath.resolve() if output_mrna_metadata_filepath else output_mrna_metadata_filepath
-    )
-    output_trna_count_matrix_filepath = (
-        output_trna_count_matrix_filepath.resolve()
-        if output_trna_count_matrix_filepath
-        else output_trna_count_matrix_filepath
-    )
-    output_mrna_count_matrix_filepath = (
-        output_mrna_count_matrix_filepath.resolve()
-        if output_mrna_count_matrix_filepath
-        else output_mrna_count_matrix_filepath
-    )
+    output_trna_metadata_filepath = output_trna_metadata_filepath.resolve() if output_trna_metadata_filepath else None
+    output_mrna_metadata_filepath = output_mrna_metadata_filepath.resolve() if output_mrna_metadata_filepath else None
+    output_trna_count_matrix_filepath = output_trna_count_matrix_filepath.resolve() if output_trna_count_matrix_filepath else None
+    output_mrna_count_matrix_filepath = output_mrna_count_matrix_filepath.resolve() if output_mrna_count_matrix_filepath else None
 
     await _process(
         context_name=context_name,

From 8c71b577de32c559d72e139d4aa9185d56162836 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 3 Jun 2025 16:20:34 -0500
Subject: [PATCH 293/345] refactor: add logging

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index e4c7c416..aa4aaf32 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -643,10 +643,7 @@ async def read_counts(file: Path) -> list[str]:
         # Remove NA values from entrez_gene_id dataframe column
         return conversion["entrez_gene_id"].dropna().tolist()
 
-    logger.info(
-        "Fetching gene info - "
-        "this can take up to 5 minutes depending on the number of genes and your internet connection"
-    )
+    logger.info("Fetching gene info - this can take up to 5 minutes depending on the number of genes and your internet connection")
     genes = set(chain.from_iterable(await asyncio.gather(*[read_counts(f) for f in counts_matrix_filepaths])))
     gene_data = await MyGene(cache=cache).query(items=list(genes), taxon=taxon, scopes="entrezgene")
     gene_info: pd.DataFrame = pd.DataFrame(

From b632a7257a57025108528142131bedc3ad1584df Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 3 Jun 2025 16:22:17 -0500
Subject: [PATCH 294/345] refactor: added slots to dataclass for reduced memory
 usage; ruff formatting

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 80 ++++++++++++++++++-----------------------
 1 file changed, 34 insertions(+), 46 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 13181f30..e7f109e7 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -45,7 +45,7 @@ class LayoutMethod(Enum):
     single_end = "single-end"
 
 
-@dataclass
+@dataclass(slots=True)
 class _StudyMetrics:
     study: str
     num_samples: int
@@ -118,7 +118,7 @@ def k_over_a(k: int, a: float) -> Callable[[npt.NDArray], bool]:
     :param k: The minimum number of times the sum of elements must be greater than or equal to A.
     :param a: The value to compare the sum of elements to.
     :return: A function that accepts a NumPy array to perform the actual filtering
-    """  # noqa: E501
+    """
 
     def filter_func(row: npt.NDArray) -> bool:
         return np.sum(row >= a) >= k
@@ -142,11 +142,7 @@ def genefilter(data: pd.DataFrame | npt.NDArray, filter_func: Callable[[npt.NDAr
             level=LogLevel.CRITICAL,
         )
 
-    return (
-        data.apply(filter_func, axis=1).values
-        if isinstance(data, pd.DataFrame)
-        else np.apply_along_axis(filter_func, axis=1, arr=data)
-    )
+    return data.apply(filter_func, axis=1).values if isinstance(data, pd.DataFrame) else np.apply_along_axis(filter_func, axis=1, arr=data)
 
 
 async def _build_matrix_results(
@@ -233,7 +229,7 @@ def calculate_tpm(metrics: NamedMetrics) -> NamedMetrics:
 
 
 def _calculate_fpkm(metrics: NamedMetrics, scale: int = 1e6) -> NamedMetrics:
-    """Calculate the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) for each sample in the metrics dictionary."""  # noqa: E501
+    """Calculate the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) for each sample in the metrics dictionary."""
     for study in metrics:
         matrix_values = []
         for sample in range(metrics[study].num_samples):
@@ -277,7 +273,6 @@ def _calculate_fpkm(metrics: NamedMetrics, scale: int = 1e6) -> NamedMetrics:
     return metrics
 
 
-# def _zfpkm_calculation(row: pd.Series, kernel: KernelDensity, peak_parameters: tuple[float, float]) -> _ZFPKMResult:
 def _zfpkm_calculation(
     column: pd.Series,
     peak_parameters: PeakIdentificationParameters,
@@ -356,10 +351,7 @@ def zfpkm_transform(
 ) -> tuple[dict[str, _ZFPKMResult], DataFrame]:
     """Perform zFPKM calculation/transformation."""
     if update_every_percent > 1:
-        logger.warning(
-            f"update_every_percent should be a decimal value between 0 and 1; got: {update_every_percent} - "
-            f"will convert to percentage"
-        )
+        logger.warning(f"update_every_percent should be a decimal value between 0 and 1; got: {update_every_percent} - will convert to percentage")
         update_every_percent /= 100
 
     total_samples = _num_columns(fpkm_df)
@@ -367,16 +359,13 @@ def zfpkm_transform(
 
     # Get at least 1 core and at most cpu_count() - 2
     cores = max(min(multiprocessing.cpu_count() - 2, total_samples), 1)
-    logger.debug(
-        f"zFPKM transforming {len(fpkm_df.columns)} sample(s) "
-        f"containing {len(fpkm_df):,} genes(s) using {cores} core(s)"
-    )
+    logger.debug(f"zFPKM transforming {len(fpkm_df.columns)} sample(s) containing {len(fpkm_df):,} genes(s) using {cores} core(s)")
     logger.debug(f"Will update every {update_per_step:,} steps (~{update_every_percent:.1%} of {total_samples:,})")
 
     chunk_time = time.time()
     start_time = time.time()
     log_padding = len(str(f"{total_samples:,}"))
-    zfpkm_series: list[pd.Series | None] = [None] * total_samples
+    zfpkm_series: list[pd.Series] = []
     results: dict[str, _ZFPKMResult] = {}
 
     with ProcessPoolExecutor(max_workers=cores) as pool:
@@ -393,7 +382,7 @@ def zfpkm_transform(
             result = future.result()
             key = str(result.zfpkm.name)
             results[key] = result
-            zfpkm_series[i] = result.zfpkm
+            zfpkm_series.append(result.zfpkm)
 
             if i != 0 and ((i + 1) % update_per_step == 0 or (i + 1) == total_samples):
                 current_time = time.time()
@@ -522,11 +511,7 @@ def cpm_filter(
         top_samples = round(n_top * len(counts.columns))  # noqa: F841
         test_bools = pd.DataFrame({"entrez_gene_ids": entrez_ids})
         for i in range(len(counts_per_million.columns)):
-            cutoff = (
-                10e6 / (np.median(np.sum(counts[:, i])))
-                if cut_off == "default"
-                else (1e6 * cut_off) / np.median(np.sum(counts[:, i]))
-            )
+            cutoff = 10e6 / (np.median(np.sum(counts[:, i]))) if cut_off == "default" else (1e6 * cut_off) / np.median(np.sum(counts[:, i]))
             test_bools = test_bools.merge(counts_per_million[counts_per_million.iloc[:, i] > cutoff])
 
     return metrics
@@ -595,28 +580,28 @@ def zfpkm_filter(
     cut_off = filtering_options.cut_off
     metrics = _calculate_fpkm(metrics) if calculate_fpkm else metrics
 
-    metric: _StudyMetrics
     for metric in metrics.values():
+        metric: _StudyMetrics
         # if fpkm was not calculated, the normalization matrix will be empty; collect the count matrix instead
         matrix = metric.count_matrix if metric.normalization_matrix.empty else metric.normalization_matrix
         matrix = matrix[matrix.sum(axis=1) > 0]  # remove rows (genes) that have no counts across all samples
 
-        minimums = matrix == 0
         results, zfpkm_df = zfpkm_transform(matrix, peak_parameters=peak_parameters, bandwidth=bandwidth)
-        zfpkm_df[minimums] = -4
+        zfpkm_df[(matrix == 0) | (zfpkm_df.isna())] = -4
 
         if len(results) > 10 and not force_zfpkm_plot:
             logger.warning(
                 "Not plotting zFPKM results because more than 10 plots would be created. "
                 "If you would like to plot them anyway, set 'force_zfpkm_plot' to True"
             )
+        elif output_png_filepath is None:
+            logger.critical("Output zFPKM PNG filepath is None, set a path to plot zFPKM graphs")
         else:
-            if output_png_filepath is None:
-                logger.critical("Output zFPKM PNG filepath is None, set a path to plot zFPKM graphs")
-            else:
-                output_png_filepath.parent.mkdir(parents=True, exist_ok=True)
-                output_png_filepath.unlink(missing_ok=True)
-                zfpkm_plot(results, output_png_filepath=output_png_filepath)
+            output_png_filepath.parent.mkdir(parents=True, exist_ok=True)
+            output_png_filepath.unlink(missing_ok=True)
+            zfpkm_plot(results, output_png_filepath=output_png_filepath)
+
+        metric.z_score_matrix = zfpkm_df
 
         # determine which genes are expressed
         min_samples = round(min_sample_expression * len(zfpkm_df.columns))
@@ -759,16 +744,25 @@ async def _process(
                 )
             )
 
+        merged_zscore_df = (
+            metric.z_score_matrix
+            if merged_zscore_df.empty
+            else merged_zscore_df.merge(
+                metric.z_score_matrix,
+                how="outer",
+                left_index=True,
+                right_index=True,
+            )
+        )
+    merged_zscore_df[merged_zscore_df.isna()] = -4
+
     # If any of the normalization metrics are not empty, write the normalized metrics to disk
     if not all(metric.normalization_matrix.empty for metric in metrics.values()):
-        merged_zscore_df.index = pd.Series(entrez_gene_ids, name="entrez_gene_id")
-        merged_zscore_df.dropna(inplace=True)
         merged_zscore_df.to_csv(output_zscore_normalization_filepath, index=True)
         logger.success(f"Wrote z-score normalization matrix to {output_zscore_normalization_filepath}")
     else:
         logger.warning(
-            "Not writing z-score normalization matrix because no normalization matrices exist. "
-            "This is expected if you are using UMI filtering."
+            "Not writing z-score normalization matrix because no normalization matrices exist. This is expected if you are using UMI filtering."
         )
 
     expression_frequency = pd.Series(expressed_genes).value_counts()
@@ -791,14 +785,11 @@ async def _process(
             boolean_matrix.loc[gene, "high"] = 1
 
     expressed_count = len(boolean_matrix[boolean_matrix["expressed"] == 1])
-    high_confidence_count = len(boolean_matrix[boolean_matrix["high"] == 1]) - expressed_count
+    high_confidence_count = len(boolean_matrix[boolean_matrix["high"] == 1])
 
     boolean_matrix.dropna(subset="entrez_gene_id", inplace=True)
     boolean_matrix.to_csv(output_boolean_activity_filepath, index=False)
-    logger.info(
-        f"{context_name} - Found {expressed_count} expressed genes, "
-        f"{high_confidence_count} of which are confidently expressed"
-    )
+    logger.info(f"{context_name} - Found {expressed_count} expressed genes, {high_confidence_count} of which are confidently expressed")
     logger.success(f"Wrote boolean matrix to {output_boolean_activity_filepath}")
 
 
@@ -909,10 +900,7 @@ async def rnaseq_gen(  # noqa: C901
     elif isinstance(input_metadata_filepath_or_df, Path):
         if input_metadata_filepath_or_df.suffix not in {".xlsx", ".xls"}:
             _log_and_raise_error(
-                (
-                    f"Expected an excel file with extension of '.xlsx' or '.xls', "
-                    f"got '{input_metadata_filepath_or_df.suffix}'."
-                ),
+                f"Expected an excel file with extension of '.xlsx' or '.xls', got '{input_metadata_filepath_or_df.suffix}'.",
                 error=ValueError,
                 level=LogLevel.ERROR,
             )

From f651a85b8812e29ba5fd5618dbfa691fde885f49 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 3 Jun 2025 16:22:53 -0500
Subject: [PATCH 295/345] refactor: better managing/converting of ensembl and
 entrez gene IDs

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 26 ++++++++++++++++----------
 1 file changed, 16 insertions(+), 10 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index e7f109e7..97b64958 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -164,7 +164,22 @@ async def _build_matrix_results(
     conversion["ensembl_gene_id"] = conversion["ensembl_gene_id"].str.split(",")
     conversion = conversion.explode("ensembl_gene_id")
     conversion.reset_index(inplace=True, drop=True)
-    matrix = matrix.merge(conversion, on=["ensembl_gene_id", "entrez_gene_id", "gene_symbol"], how="left")
+
+    merge_on = []
+    if "ensembl_gene_id" in matrix.columns and "ensembl_gene_id" in conversion.columns:
+        merge_on.append("ensembl_gene_id")
+    if "entrez_gene_id" in matrix.columns and "entrez_gene_id" in conversion.columns:
+        merge_on.append("entrez_gene_id")
+    if "gene_symbol" in matrix.columns and "gene_symbol" in conversion.columns:
+        merge_on.append("gene_symbol")
+
+    if not merge_on:
+        _log_and_raise_error(
+            "No columns to merge on. Tested 'ensembl_gene_id', 'entrez_gene_id', and 'gene_symbol'. Please check your input files.",
+            error=ValueError,
+            level=LogLevel.ERROR,
+        )
+    matrix = matrix.merge(conversion, on=merge_on, how="left")
 
     # Only include Entrez and Ensembl Gene IDs that are present in `gene_info`
     matrix["entrez_gene_id"] = matrix["entrez_gene_id"].str.split("//")
@@ -734,15 +749,6 @@ async def _process(
     for metric in metrics.values():
         expressed_genes.extend(metric.entrez_gene_ids)
         top_genes.extend(metric.high_confidence_entrez_gene_ids)
-        if not metric.normalization_matrix.empty:
-            merged_zscore_df = (
-                metric.z_score_matrix
-                if merged_zscore_df.empty
-                else pd.concat(
-                    [merged_zscore_df, metric.z_score_matrix],
-                    axis=1,
-                )
-            )
 
         merged_zscore_df = (
             metric.z_score_matrix

From 5a30cede35b0270755c7c18f1db2a5ace44361c4 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 3 Jun 2025 16:23:19 -0500
Subject: [PATCH 296/345] refactor: more efficient tpm calculation

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 15 ++++++---------
 1 file changed, 6 insertions(+), 9 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 97b64958..f4372c9b 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -229,15 +229,12 @@ def calculate_tpm(metrics: NamedMetrics) -> NamedMetrics:
     for sample in metrics:
         count_matrix = metrics[sample].count_matrix
 
-        gene_sizes = metrics[sample].gene_sizes
-
-        tpm_matrix = pd.DataFrame(data=None, index=count_matrix.index, columns=count_matrix.columns)
-        for i in range(len(count_matrix.columns)):
-            values: pd.Series = count_matrix.iloc[:, i] + 1  # Add 1 to prevent division by 0
-            rate = np.log(values.tolist()) - np.log(gene_sizes)
-            denominator = np.log(np.sum(np.exp(rate)))
-            tpm_value = np.exp(rate - denominator + np.log(1e6))
-            tpm_matrix.iloc[:, i] = tpm_value
+        gene_sizes = pd.Series(metrics[sample].gene_sizes, index=count_matrix.index)
+        adjusted_counts = count_matrix.add(1e-6)
+
+        tpm_matrix = adjusted_counts.divide(gene_sizes, axis=0)  # (count + 1) / gene_length
+        tpm_matrix = tpm_matrix.div(tpm_matrix.sum(axis=0), axis=1)  # normalize by total
+        tpm_matrix = tpm_matrix.mul(1e6)  # scale to per-million
         metrics[sample].normalization_matrix = tpm_matrix
 
     return metrics

From 645eeef3360bec63a3869c953abc3051cf497ed7 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 3 Jun 2025 16:23:57 -0500
Subject: [PATCH 297/345] refactor: use pandas dataframes instead of numpy
 arrays for developer ease-of-use

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 19 +++++++++++++------
 1 file changed, 13 insertions(+), 6 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index f4372c9b..57424d3a 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -246,7 +246,7 @@ def _calculate_fpkm(metrics: NamedMetrics, scale: int = 1e6) -> NamedMetrics:
         matrix_values = []
         for sample in range(metrics[study].num_samples):
             layout = metrics[study].layout[sample]
-            count_matrix: npt.NDArray = metrics[study].count_matrix.iloc[:, sample].values.astype(np.float32)
+            count_matrix: pd.DataFrame = metrics[study].count_matrix.iloc[:, sample].values.astype(np.float32)
             gene_lengths = (
                 metrics[study].fragment_lengths[sample].astype(np.float32)
                 if layout == LayoutMethod.paired_end
@@ -257,12 +257,15 @@ def _calculate_fpkm(metrics: NamedMetrics, scale: int = 1e6) -> NamedMetrics:
             match layout:
                 case LayoutMethod.paired_end:  # FPKM
                     total_fragments = count_matrix.sum(axis=0)
-                    counts_per_million = total_fragments / scale
-                    fragments_per_kilobase = count_matrix / gene_lengths_kb[:, np.newaxis]
-                    fragments_per_kilobase_million = fragments_per_kilobase / counts_per_million
+                    if total_fragments == 0:
+                        fragments_per_kilobase_million = np.nan
+                    else:
+                        counts_per_million = total_fragments / scale
+                        fragments_per_kilobase = count_matrix / gene_lengths_kb
+                        fragments_per_kilobase_million = fragments_per_kilobase / counts_per_million
                     matrix_values.append(fragments_per_kilobase_million)
                 case LayoutMethod.single_end:  # RPKM
-                    reads_per_kilobase = count_matrix / gene_lengths_kb[: np.newaxis]
+                    reads_per_kilobase = count_matrix / gene_lengths_kb
                     total_reads = count_matrix.sum(axis=0)
                     counts_per_million = total_reads / scale
                     reads_per_kilobase_million = reads_per_kilobase / counts_per_million
@@ -277,7 +280,11 @@ def _calculate_fpkm(metrics: NamedMetrics, scale: int = 1e6) -> NamedMetrics:
                         level=LogLevel.ERROR,
                     )
 
-        fpkm_matrix = pd.DataFrame(matrix_values).T  # Transpose is needed because values were appended as rows
+        # Transpose is needed because values were appended as rows
+        fpkm_matrix = pd.DataFrame(matrix_values).T
+        fpkm_matrix.index = metrics[study].count_matrix.index
+        fpkm_matrix.columns = metrics[study].sample_names
+
         fpkm_matrix = fpkm_matrix[~pd.isna(fpkm_matrix)]
         metrics[study].normalization_matrix = fpkm_matrix
         metrics[study].normalization_matrix.columns = metrics[study].count_matrix.columns

From 146a8069039a22454ff4185f75f9d5960cfb3312 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 3 Jun 2025 16:24:17 -0500
Subject: [PATCH 298/345] refactor: fix plot generation

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 57424d3a..a3c798c3 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -453,11 +453,11 @@ def zfpkm_plot(results, *, output_png_filepath: Path, plot_xfloor: int = -4):
     mega_df.columns = pd.Series(data=["sample_name", "log2fpkm", "fpkm_density", "fitted_density_scaled"])
     mega_df = mega_df.melt(id_vars=["log2fpkm", "sample_name"], var_name="source", value_name="density")
 
-    fig, axes = plt.subplot(len(results), 1, figsize=(8, 4 * len(results)))
+    fig, axes = plt.subplots(nrows=len(results), ncols=1, figsize=(8, 4 * len(results)))
     if len(results) == 1:
         axes = [axes]
 
-    for i, sample_name in enumerate(results, start=1):
+    for i, sample_name in enumerate(results):
         sample_data = mega_df[mega_df["sample_name"] == sample_name]
         axis = axes[i]
 
@@ -472,9 +472,7 @@ def zfpkm_plot(results, *, output_png_filepath: Path, plot_xfloor: int = -4):
 
     plt.tight_layout()
     if output_png_filepath.suffix != ".png":
-        logger.warning(
-            f"Output filepath did not end in '.png', setting to '.png' now. Got: '{output_png_filepath.suffix}'"
-        )
+        logger.warning(f"Output filepath did not end in '.png', setting to '.png' now. Got: '{output_png_filepath.suffix}'")
         output_png_filepath = output_png_filepath.with_suffix(".png")
     plt.savefig(output_png_filepath)
 
@@ -483,9 +481,7 @@ def calculate_z_score(metrics: NamedMetrics) -> NamedMetrics:
     """Calculate the z-score for each sample in the metrics dictionary."""
     for sample in metrics:
         log_matrix = np.log(metrics[sample].normalization_matrix)
-        z_matrix = pd.DataFrame(
-            data=sklearn.preprocessing.scale(log_matrix, axis=1), columns=metrics[sample].sample_names
-        )
+        z_matrix = pd.DataFrame(data=sklearn.preprocessing.scale(log_matrix, axis=1), columns=metrics[sample].sample_names)
         metrics[sample].z_score_matrix = z_matrix
     return metrics
 

From a9d78007bf341c93a5f597a3c5b56ba292041b6d Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 3 Jun 2025 16:24:42 -0500
Subject: [PATCH 299/345] refactor: ruff formatting

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 21 ++++++---------------
 1 file changed, 6 insertions(+), 15 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index a3c798c3..e2e988d4 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -552,12 +552,8 @@ def tpm_quantile_filter(*, metrics: NamedMetrics, filtering_options: _FilteringO
         top_samples = round(n_top * len(tpm_matrix.columns))
 
         tpm_quantile = tpm_matrix[tpm_matrix > 0]
-        quantile_cutoff = np.quantile(
-            a=tpm_quantile.values, q=1 - (cut_off / 100), axis=0
-        )  # Compute quantile across columns
-        boolean_expression = pd.DataFrame(
-            data=tpm_matrix > quantile_cutoff, index=tpm_matrix.index, columns=tpm_matrix.columns
-        ).astype(int)
+        quantile_cutoff = np.quantile(a=tpm_quantile.values, q=1 - (cut_off / 100), axis=0)  # Compute quantile across columns
+        boolean_expression = pd.DataFrame(data=tpm_matrix > quantile_cutoff, index=tpm_matrix.index, columns=tpm_matrix.columns).astype(int)
 
         min_func = k_over_a(min_samples, 0.9)
         top_func = k_over_a(top_samples, 0.9)
@@ -648,9 +644,7 @@ def filter_counts(
     """Filter the count matrix based on the specified technique."""
     match technique:
         case FilteringTechnique.CPM:
-            return cpm_filter(
-                context_name=context_name, metrics=metrics, filtering_options=filtering_options, prep=prep
-            )
+            return cpm_filter(context_name=context_name, metrics=metrics, filtering_options=filtering_options, prep=prep)
         case FilteringTechnique.TPM:
             return tpm_quantile_filter(metrics=metrics, filtering_options=filtering_options)
         case FilteringTechnique.ZFPKM:
@@ -707,9 +701,7 @@ async def _process(
 
     rnaseq_matrix: pd.DataFrame = await _read_file(rnaseq_matrix_filepath)
     if rnaseq_matrix_filepath.suffix == ".h5ad":
-        conversion = await gene_symbol_to_ensembl_and_gene_id(
-            symbols=rnaseq_matrix["gene_symbol"].tolist(), taxon=taxon
-        )
+        conversion = await gene_symbol_to_ensembl_and_gene_id(symbols=rnaseq_matrix["gene_symbol"].tolist(), taxon=taxon)
         conversion.reset_index(inplace=True)
         rnaseq_matrix = rnaseq_matrix.merge(conversion, how="left", on="gene_symbol")
         rnaseq_matrix.replace(to_replace=pd.NA, value="-")
@@ -728,6 +720,7 @@ async def _process(
         metadata_df=metadata_df,
         taxon=taxon,
     )
+
     metrics = read_counts_results.metrics
     entrez_gene_ids = read_counts_results.entrez_gene_ids
 
@@ -772,9 +765,7 @@ async def _process(
         )
 
     expression_frequency = pd.Series(expressed_genes).value_counts()
-    expression_df = pd.DataFrame(
-        {"entrez_gene_id": expression_frequency.index, "frequency": expression_frequency.values}
-    )
+    expression_df = pd.DataFrame({"entrez_gene_id": expression_frequency.index, "frequency": expression_frequency.values})
     expression_df["prop"] = expression_df["frequency"] / len(metrics)
     expression_df = expression_df[expression_df["prop"] >= filtering_options.batch_ratio]
 

From 992469a673883ce0fed1e3fd54ee3fe4d896197e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 3 Jun 2025 16:25:25 -0500
Subject: [PATCH 300/345] refactor: ruff formatting

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/combine_distributions.py | 32 ++++++++----------------------
 1 file changed, 8 insertions(+), 24 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 3571f122..9717eca1 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -43,10 +43,7 @@ async def _combine_z_distribution_for_batch(
         f"context: '{context_name}'"
     )
     if _num_columns(matrix) < 2:
-        logger.trace(
-            f"A single sample exists for batch '{batch.batch_num}'. "
-            f"Returning as-is because no additional combining can be done"
-        )
+        logger.trace(f"A single sample exists for batch '{batch.batch_num}'. Returning as-is because no additional combining can be done")
         return matrix
 
     values = matrix.iloc[:, 1:].values
@@ -147,16 +144,12 @@ def _combine_z_distribution_for_context(
         return pd.DataFrame({"ensembl_gene_id": [], "combine_z": []})
 
     z_matrices = [
-        res.z_score_matrix.set_index("ensembl_gene_id").rename(
-            columns={col: res.type.value for col in res.z_score_matrix.columns[1:]}
-        )
+        res.z_score_matrix.set_index("ensembl_gene_id").rename(columns={col: res.type.value for col in res.z_score_matrix.columns[1:]})
         for res in zscore_results
     ]
     z_matrix = pd.concat(z_matrices, axis=1, join="outer").reset_index()
     if _num_columns(z_matrix) <= 1:
-        logger.trace(
-            f"Only 1 source exists for '{context}', returning dataframe as-is becuase no data exists to combine"
-        )
+        logger.trace(f"Only 1 source exists for '{context}', returning dataframe as-is becuase no data exists to combine")
         z_matrix.columns = ["ensembl_gene_id", "combine_z"]
         return z_matrix
 
@@ -232,8 +225,7 @@ async def _begin_combining_distributions(
                     matrix=matrix[[GeneIdentifier.ENSEMBL_GENE_ID.value, *batch.sample_names]],
                     source=source,
                     output_combined_matrix_filepath=(
-                        output_filepaths[source.value].parent
-                        / f"{context_name}_{source.value}_batch{batch.batch_num}_combined_z_distribution_.csv"
+                        output_filepaths[source.value].parent / f"{context_name}_{source.value}_batch{batch.batch_num}_combined_z_distribution_.csv"
                     ),
                     output_figure_dirpath=output_figure_dirpath,
                     weighted_z_floor=weighted_z_floor,
@@ -245,21 +237,16 @@ async def _begin_combining_distributions(
 
         merged_batch_results = pd.DataFrame()
         for df in batch_results:
-            merged_batch_results = (
-                df if merged_batch_results.empty else merged_batch_results.merge(df, on="ensembl_gene_id", how="outer")
-            )
+            merged_batch_results = df if merged_batch_results.empty else merged_batch_results.merge(df, on="ensembl_gene_id", how="outer")
 
         merged_source_results: pd.DataFrame = await _combine_z_distribution_for_source(
             merged_source_data=merged_batch_results,
             context_name=context_name,
             num_replicates=sum(batch.num_samples for batch in batch_names[source.value]),
             output_combined_matrix_filepath=(
-                output_filepaths[source.value].parent
-                / f"{context_name}_{source.value}_combined_zscore_distribution.csv"
-            ),
-            output_figure_filepath=(
-                output_figure_dirpath / f"{context_name}_{source.value}_combined_zscore_distribution.pdf"
+                output_filepaths[source.value].parent / f"{context_name}_{source.value}_combined_zscore_distribution.csv"
             ),
+            output_figure_filepath=(output_figure_dirpath / f"{context_name}_{source.value}_combined_zscore_distribution.pdf"),
             weighted_z_floor=weighted_z_floor,
             weighted_z_ceiling=weighted_z_ceiling,
         )
@@ -271,10 +258,7 @@ async def _begin_combining_distributions(
             )
         )
         merged_source_results.to_csv(output_filepaths[source.value])
-        logger.success(
-            f"Wrote z-scores for source '{source.value}' "
-            f"in context '{context_name}' to '{output_filepaths[source.value]}'"
-        )
+        logger.success(f"Wrote z-scores for source '{source.value}' in context '{context_name}' to '{output_filepaths[source.value]}'")
 
     logger.trace(f"Combining z-score distributions for all sources in context '{context_name}'")
     merged_context_results = _combine_z_distribution_for_context(

From 8c00a1fc08b160f8cc43b20987e650b12721dd49 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 3 Jun 2025 16:26:37 -0500
Subject: [PATCH 301/345] refactor: ruff formatting

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/create_context_specific_model.py | 10 ++--------
 1 file changed, 2 insertions(+), 8 deletions(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 1860745e..5a46ab13 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -178,10 +178,7 @@ def _build_with_fastcore(cobra_model, s_matrix, lower_bounds, upper_bounds, exp_
     # 'Vlassis, Pacheco, Sauter (2014). Fast reconstruction of compact
     # context-specific metabolic network models. PLoS Comput. Biol. 10,
     # e1003424.'
-    logger.warning(
-        "Fastcore requires a flux consistant model is used as refererence, "
-        "to achieve this fastcc is required which is NOT reproducible."
-    )
+    logger.warning("Fastcore requires a flux consistant model is used as refererence, to achieve this fastcc is required which is NOT reproducible.")
     logger.debug("Creating feasible model")
     incon_rxns, cobra_model = _feasibility_test(cobra_model, "other")
     properties = FastcoreProperties(core=exp_idx_list, solver=solver)
@@ -358,7 +355,6 @@ async def _build_model(  # noqa: C901
     reference_model.objective = {getattr(reference_model.reactions, objective): 1}
     if objective not in force_reactions:
         force_reactions.append(objective)
-
     reference_model = _set_boundaries(reference_model, boundary_reactions, lower_bounds, upper_bounds)
     reference_model.solver = solver.lower()
 
@@ -587,9 +583,7 @@ async def create_context_specific_model(  # noqa: C901
 ):
     """Create a context-specific model using the provided data."""
     _set_up_logging(level=log_level, location=log_location)
-    output_model_filepaths = (
-        [output_model_filepaths] if isinstance(output_model_filepaths, Path) else output_model_filepaths
-    )
+    output_model_filepaths = [output_model_filepaths] if isinstance(output_model_filepaths, Path) else output_model_filepaths
     for path in output_model_filepaths:
         if path.suffix not in {".mat", ".xml", ".sbml", ".json"}:
             _log_and_raise_error(

From d6517004cc70dcf5d572130c452279f63c243134 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 3 Jun 2025 16:27:02 -0500
Subject: [PATCH 302/345] style: better variable naming

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/create_context_specific_model.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 5a46ab13..9e607c13 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -253,7 +253,7 @@ def _build_with_tinit(
 
 
 async def _map_expression_to_reaction(
-    model_cobra,
+    reference_model,
     gene_expression_file,
     recon_algorithm: Algorithm,
     low_thresh: float,
@@ -277,7 +277,7 @@ async def _map_expression_to_reaction(
     # fmt: on
 
     error_count = 0
-    for rxn in model_cobra.reactions:
+    for rxn in reference_model.reactions:
         rxn: cobra.Reaction
         gene_reaction_rule = _correct_bracket(rxn.gene_reaction_rule, rxn.gene_name_reaction_rule)
         if gene_reaction_rule == "":
@@ -352,7 +352,6 @@ async def _build_model(  # noqa: C901
                 level=LogLevel.ERROR,
             )
 
-    reference_model.objective = {getattr(reference_model.reactions, objective): 1}
     if objective not in force_reactions:
         force_reactions.append(objective)
     reference_model = _set_boundaries(reference_model, boundary_reactions, lower_bounds, upper_bounds)

From 66bc302a5b525600d627582b8a8ee53872e32b08 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 3 Jun 2025 16:27:21 -0500
Subject: [PATCH 303/345] refactor: fix creating evaluable gene reaction rules

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/create_context_specific_model.py | 21 ++++++---------------
 1 file changed, 6 insertions(+), 15 deletions(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 9e607c13..8818280e 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -279,31 +279,22 @@ async def _map_expression_to_reaction(
     error_count = 0
     for rxn in reference_model.reactions:
         rxn: cobra.Reaction
-        gene_reaction_rule = _correct_bracket(rxn.gene_reaction_rule, rxn.gene_name_reaction_rule)
+
+        gene_reaction_rule = rxn.gene_reaction_rule
         if gene_reaction_rule == "":
             continue
 
-        gene_ids = re.findall(r"\d+", gene_reaction_rule)
+        gene_ids = set(re.findall(r"\d+", gene_reaction_rule))
         reaction_expression[rxn.id] = default_expression
         for gene_id in gene_ids:
-            activity = (
-                f"{gene_activity.at[gene_id, 'active']}"
-                if gene_id in gene_activity.index
-                else f"{default_expression!s}"
-            )
+            activity = f"{gene_activity.at[gene_id, 'active']}" if gene_id in gene_activity.index else f"{default_expression!s}"
             # replace gene_id with activity, using optional whitespace before and after the gene id
             # Do not replace the whitespace (if it exists) before and after the gene ID
-            gene_reaction_rule = re.sub(
-                pattern=rf"(?<!\S){gene_id}(?!\S)",
-                repl=activity,
-                string=gene_reaction_rule,
-                count=1,
-            )
+            gene_reaction_rule = re.sub(pattern=rf"\b{gene_id}\b", repl=activity, string=gene_reaction_rule)
 
         try:
             # We are using eval here because ast.literal_eval is unable to process an evaluable such as `max(-4, -4)`
-            # This isn't ideal, but ultimately the only other option is writing and maintaining a custom parsing engine,
-            #   which is too much work to do.
+            # This isn't ideal, but ultimately the only other option is writing and maintaining a custom parsing engine, which is too much work
             evaluable_gene_rule = _gene_rule_logical(gene_reaction_rule).replace("{", "(").replace("}", ")")
             reaction_expression[rxn.id] = eval(evaluable_gene_rule)  # noqa: S307
         except ValueError:

From a2cb161020c7c0acc00203f17480f4e95dd9db8c Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 3 Jun 2025 16:27:37 -0500
Subject: [PATCH 304/345] style: ruff formatting

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 16 +++-------------
 1 file changed, 3 insertions(+), 13 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index aa4aaf32..332e9948 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -200,9 +200,7 @@ def _organize_gene_counts_files(data_dir: Path) -> list[_StudyMetrics]:
         count_files = list(gene_dir.glob("*.tab"))
         strand_files = list(strand_dir.glob("*.txt"))
         if len(count_files) == 0:
-            _log_and_raise_error(
-                f"No count files found for study '{gene_dir.stem}'.", error=ValueError, level=LogLevel.ERROR
-            )
+            _log_and_raise_error(f"No count files found for study '{gene_dir.stem}'.", error=ValueError, level=LogLevel.ERROR)
         if len(strand_files) == 0:
             _log_and_raise_error(
                 f"No strandedness files found for study '{gene_dir.stem}'.",
@@ -330,9 +328,7 @@ async def _write_counts_matrix(
 ) -> pd.DataFrame:
     """Create a counts matrix file by reading gene counts table(s)."""
     study_metrics = _organize_gene_counts_files(data_dir=como_context_dir)
-    counts: list[pd.DataFrame] = await asyncio.gather(
-        *[_create_sample_counts_matrix(metric) for metric in study_metrics]
-    )
+    counts: list[pd.DataFrame] = await asyncio.gather(*[_create_sample_counts_matrix(metric) for metric in study_metrics])
 
     final_matrix = pd.DataFrame()
     for count in counts:
@@ -666,13 +662,7 @@ async def read_counts(file: Path) -> list[str]:
         gene_info.at[i, "ensembl_gene_id"] = ensembl_ids
         gene_info.at[i, "size"] = end_pos - start_pos
 
-    gene_info = gene_info[
-        (
-            (gene_info["entrez_gene_id"] != "-")
-            & (gene_info["ensembl_gene_id"] != "-")
-            & (gene_info["gene_symbol"] != "-")
-        )
-    ]
+    gene_info = gene_info[((gene_info["entrez_gene_id"] != "-") & (gene_info["ensembl_gene_id"] != "-") & (gene_info["gene_symbol"] != "-"))]
     gene_info.sort_values(by="ensembl_gene_id", inplace=True)
     gene_info.dropna(inplace=True)
     gene_info.to_csv(output_filepath, index=False)

From 5e331ee9733fef067df0f3cedb8c620d0a9d3dcc Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Tue, 3 Jun 2025 16:27:58 -0500
Subject: [PATCH 305/345] refactor: only publish package when tag starts with
 'v'

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 .github/workflows/container_build.yml | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/container_build.yml b/.github/workflows/container_build.yml
index 19512c76..f24b7152 100644
--- a/.github/workflows/container_build.yml
+++ b/.github/workflows/container_build.yml
@@ -11,8 +11,11 @@ env:
 
 jobs:
   build-and-push-image:
+    # IMPORTANT: Only run the publish job when a tag starting with 'v' is pushed
+    if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v')
+
     name: Build and Push Docker Image
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     permissions:
       contents: read
       packages: write

From f572104dcf0a893853a8b854b7c64065060934ff Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Thu, 7 Aug 2025 15:15:14 -0500
Subject: [PATCH 306/345] refactor: use better method of converting to numeric
 dtype

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 31 +++++++++++++++++++++++++++++--
 1 file changed, 29 insertions(+), 2 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 332e9948..b54c96f9 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -243,7 +243,7 @@ async def _process_first_multirun_sample(strand_file: Path, all_counts_files: li
 
     # Set na values to 0
     sample_count = sample_count.fillna(value="0")
-    sample_count.iloc[:, 1:] = sample_count.iloc[:, 1:].apply(pd.to_numeric)
+    sample_count["counts"] = sample_count["counts"].astype(np.float64)
 
     count_sums: pd.DataFrame = pd.DataFrame(sample_count.sum(axis=1, numeric_only=True))
     count_sums.insert(0, "ensembl_gene_id", sample_count["ensembl_gene_id"])
@@ -359,7 +359,7 @@ async def _create_config_df(  # noqa: C901
     The configuration file created is based on the gene counts matrix.
      If using zFPKM normalization technique, mean fragment lengths will be fetched
     """
-    label_regex: Final = re.compile(r"(?P<study>S\d{1,3})(?P<rep>R\d{1,3})(?:(?P<run>r\d{1,3}))?")
+    label_regex: Final = re.compile(r"(?P<study>S\d{1,3})(?P<rep>R\d{1,3})(?P<run>r\d{1,3})?")
     gene_counts: list[Path] = list((como_context_dir / gene_count_dirname).rglob("*.tab"))
     if not gene_counts:
         _log_and_raise_error(
@@ -621,6 +621,7 @@ async def _create_gene_info_file(
     """
 
     async def read_counts(file: Path) -> list[str]:
+        print(f"Will read: '{file}'")
         data = await _read_file(file, h5ad_as_df=False)
 
         try:
@@ -677,6 +678,7 @@ async def _process_como_input(
     rna: RNAType,
 ) -> None:
     config_df = await _create_config_df(context_name, como_context_dir=como_context_dir)
+
     await _write_counts_matrix(
         config_df=config_df,
         como_context_dir=como_context_dir,
@@ -796,3 +798,28 @@ async def rnaseq_preprocess(
         output_mrna_matrix_filepath=output_mrna_count_matrix_filepath,
         cache=cache,
     )
+
+
+async def _main():
+    context_name = "notreatment"
+    taxon = 9606
+    como_context_dir = Path("/Users/joshl/Projects/COMO/main/data/COMO_input/notreatment")
+    output_gene_info_filepath = Path("/Users/joshl/Projects/COMO/main/data/results/notreatment/gene_info.csv")
+    output_trna_metadata_filepath = Path("/Users/joshl/Projects/COMO/main/data/config_sheets/trna_config.xlsx")
+    output_trna_count_matrix_filepath = Path("/Users/joshl/Projects/COMO/main/data/results/notreatment/total-rna/totalrna_notreatment.csv")
+
+    await rnaseq_preprocess(
+        context_name=context_name,
+        taxon=taxon,
+        como_context_dir=como_context_dir,
+        input_matrix_filepath=None,
+        output_gene_info_filepath=output_gene_info_filepath,
+        output_trna_metadata_filepath=output_trna_metadata_filepath,
+        output_trna_count_matrix_filepath=output_trna_count_matrix_filepath,
+        cache=False,
+        log_level="INFO",
+    )
+
+
+if __name__ == "__main__":
+    asyncio.run(_main())

From 3a547eac15d162a6b2fb01b421b5b9ff18be8c94 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 5 Sep 2025 11:14:10 -0500
Subject: [PATCH 307/345] chore: bump package requirements

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 pyproject.toml |   19 +-
 uv.lock        | 3333 +++++++++++++++++++++++++++++-------------------
 2 files changed, 2014 insertions(+), 1338 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index af0963d7..739f2d61 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,29 +5,30 @@ build-backend = "hatchling.build"
 [project]
 name = "COMO"
 dynamic = ["version"]
-requires-python = ">=3.10,<3.13"
+requires-python = ">=3.11,<3.14"
 dependencies = [
     "aioftp>=0.23.1",
+    "anndata>=0.12.0",
     "cobamp@git+https://github.com/JoshLoecker/cobamp@master",
     "cobra>=0.28.0",
     "fast-bioservices>=0.3.9",
-    "kaleido==0.2.1",
+    "kaleido>=1.0.0",
     "loguru>=0.7.2",
-    "numpy<2.0",
+    "numpy>=2",
     "openpyxl>=3.1.5",
     "pandas>=1.3.5",
     "scanpy>=1.10.4",
     "scikit-learn>=1.5.2",
-    "scipy>=1.7.3",
+    "scipy>=1.13.0",
     "seaborn>=0.13.2",
-    "setuptools<60.0",
-    "statsmodels>=0.13.0",
+    "setuptools>=78.1.1",
+    "statsmodels>=0.13.0; python_version < '3.12'",
     "statsmodels>=0.14.0; python_version >= '3.12'",
     "troppo@git+https://github.com/JoshLoecker/troppo@master",
 ]
 
 [project.optional-dependencies]
-gurobi = [ "gurobipy>=10.0.0" ]
+gurobi = [ "gurobipy<14" ]
 interactive =   [
     "ipython>=8.0.0",
     "jupyterlab>=4.3.2"
@@ -64,8 +65,12 @@ update_changelog_on_bump = true
 
 [tool.uv.sources]
 como = { workspace = true }
+cobra = { git = "https://github.com/ripaul/cobrapy", rev = "hopsy-backend" }
 
 [dependency-groups]
 dev = [
     "como",
+    "pandas-stubs>=2.3.2.250827",
+    "pre-commit>=4.2.0",
+    "scipy-stubs>=1.16.1.1",
 ]
diff --git a/uv.lock b/uv.lock
index 1a227653..e2c23f0c 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,139 +1,139 @@
 version = 1
-requires-python = ">=3.10, <3.13"
+revision = 2
+requires-python = ">=3.11, <3.14"
 resolution-markers = [
     "python_full_version >= '3.12'",
-    "python_full_version == '3.11.*'",
-    "python_full_version < '3.11'",
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 [[package]]
 name = "como"
-version = "1.11.1"
 source = { editable = "." }
 dependencies = [
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+    { name = "anndata" },
     { name = "cobamp" },
     { name = "cobra" },
     { name = "fast-bioservices" },
+    { name = "gurobipy" },
+    { name = "hopsy" },
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@@ -455,9 +483,6 @@ dev = [
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     { name = "jupyterlab" },
@@ -466,23 +491,28 @@ interactive = [
 [package.dev-dependencies]
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     { name = "como" },
+    { name = "pandas-stubs" },
+    { name = "pre-commit" },
+    { name = "scipy-stubs" },
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-    { name = "gurobipy", marker = "extra == 'gurobi'", specifier = ">=10.0.0" },
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     { name = "scikit-learn", specifier = ">=1.5.2" },
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+    { name = "scipy" },
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+dependencies = [
+    { name = "donfig" },
+    { name = "numcodecs", extra = ["crc32c"] },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "typing-extensions" },
+]
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From ca99e0a323c0143db7606b2518b952d7379f8fb5 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 5 Sep 2025 11:14:28 -0500
Subject: [PATCH 308/345] fix: swap minimum and maximum bounds for exchange
 reactions

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 .../boundary_rxns/naiveB_boundary_rxns.csv    | 102 +++++++++---------
 main/data/boundary_rxns/smB_boundary_rxns.csv |   4 +-
 2 files changed, 53 insertions(+), 53 deletions(-)

diff --git a/main/data/boundary_rxns/naiveB_boundary_rxns.csv b/main/data/boundary_rxns/naiveB_boundary_rxns.csv
index 0b08556e..be3eec35 100644
--- a/main/data/boundary_rxns/naiveB_boundary_rxns.csv
+++ b/main/data/boundary_rxns/naiveB_boundary_rxns.csv
@@ -1,7 +1,7 @@
-Boundary,Abbreviation,Compartment,Minimum Reaction Rate,Maximum Reaction Rate
-Exchange,glc_D,Extracellular,-100,1000
+Reaction,Abbreviation,Compartment,Minimum Reaction Rate,Maximum Reaction Rate
+Exchange,glc_D,Extracellular,-1000,100
 Exchange,fe2,Extracellular,-1000,1000
-Exchange,gal,Extracellular,0,1000
+Exchange,gal,Extracellular,-1000,0
 Exchange,zn2,Extracellular,-1000,1000
 Exchange,ca2,Extracellular,-1000,1000
 Exchange,na1,Extracellular,-1000,1000
@@ -16,55 +16,55 @@ Exchange,co2,Extracellular,-1000,1000
 Exchange,nh4,Extracellular,-1000,1000
 Exchange,no2,Extracellular,-1000,1000
 Exchange,co,Extracellular,-1000,1000
-Exchange,asn_L,Extracellular,-1,1000
-Exchange,asp_L,Extracellular,-1,1000
-Exchange,glu_L,Extracellular,-1,1000
-Exchange,ile_L,Extracellular,-1,1000
-Exchange,leu_L,Extracellular,-1,1000
-Exchange,lys_L,Extracellular,-1,1000
-Exchange,met_L,Extracellular,-1,1000
-Exchange,pro_L,Extracellular,-1,1000
-Exchange,ser_L,Extracellular,-1,1000
-Exchange,val_L,Extracellular,-1,1000
-Exchange,gly,Extracellular,-1,1000
-Exchange,cys_L,Extracellular,-1,1000
-Exchange,ala_L,Extracellular,-1,1000
-Exchange,his_L,Extracellular,-1,1000
-Exchange,thr_L,Extracellular,-1,1000
-Exchange,gln_L,Extracellular,-1,1000
-Exchange,phe_L,Extracellular,-1,1000
-Exchange,tyr_L,Extracellular,-1,1000
-Exchange,arg_L,Extracellular,-1,1000
-Exchange,trp_L,Extracellular,-1,1000
-Exchange,eicostet,Extracellular,-1,1000
-Exchange,hdca,Extracellular,-1,1000
-Exchange,hdcea,Extracellular,-1,1000
-Exchange,lnlc,Extracellular,-1,1000
-Exchange,lnlnca,Extracellular,-1,1000
-Exchange,lnlncg,Extracellular,-1,1000
-Exchange,ocdca,Extracellular,-1,1000
-Exchange,ocdcea,Extracellular,-1,1000
-Exchange,thmmp,Extracellular,0,1000
-Exchange,thmtp,Extracellular,0,1000
-Exchange,ncam,Extracellular,0,1000
-Exchange,pnto_R,Extracellular,0,1000
-Exchange,pydxn,Extracellular,0,1000
-Exchange,pydx,Extracellular,0,1000
-Exchange,pydam,Extracellular,0,1000
-Exchange,btn,Extracellular,0,1000
-Exchange,fol,Extracellular,-10,1000
-Exchange,aqcobal,Extracellular,0,1000
-Exchange,vitd3,Extracellular,-1,1000
-Exchange,ascb_L,Extracellular,0,1000
-Exchange,retinol,Extracellular,-10,1000
-Exchange,retinal,Extracellular,-10,1000
-Exchange,ala_B,Extracellular,-1,1000
-Exchange,ala_D,Extracellular,-1,1000
+Exchange,asn_L,Extracellular,-1000,1
+Exchange,asp_L,Extracellular,-1000,1
+Exchange,glu_L,Extracellular,-1000,1
+Exchange,ile_L,Extracellular,-1000,1
+Exchange,leu_L,Extracellular,-1000,1
+Exchange,lys_L,Extracellular,-1000,1
+Exchange,met_L,Extracellular,-1000,1
+Exchange,pro_L,Extracellular,-1000,1
+Exchange,ser_L,Extracellular,-1000,1
+Exchange,val_L,Extracellular,-1000,1
+Exchange,gly,Extracellular,-1000,1
+Exchange,cys_L,Extracellular,-1000,1
+Exchange,ala_L,Extracellular,-1000,1
+Exchange,his_L,Extracellular,-1000,1
+Exchange,thr_L,Extracellular,-1000,1
+Exchange,gln_L,Extracellular,-1000,1
+Exchange,phe_L,Extracellular,-1000,1
+Exchange,tyr_L,Extracellular,-1000,1
+Exchange,arg_L,Extracellular,-1000,1
+Exchange,trp_L,Extracellular,-1000,1
+Exchange,eicostet,Extracellular,-1000,1
+Exchange,hdca,Extracellular,-1000,1
+Exchange,hdcea,Extracellular,-1000,1
+Exchange,lnlc,Extracellular,-1000,1
+Exchange,lnlnca,Extracellular,-1000,1
+Exchange,lnlncg,Extracellular,-1000,1
+Exchange,ocdca,Extracellular,-1000,1
+Exchange,ocdcea,Extracellular,-1000,1
+Exchange,thmmp,Extracellular,-1000,0
+Exchange,thmtp,Extracellular,-1000,0
+Exchange,ncam,Extracellular,-1000,0
+Exchange,pnto_R,Extracellular,-1000,0
+Exchange,pydxn,Extracellular,-1000,0
+Exchange,pydx,Extracellular,-1000,0
+Exchange,pydam,Extracellular,-1000,0
+Exchange,btn,Extracellular,-1000,0
+Exchange,fol,Extracellular,-1000,10
+Exchange,aqcobal,Extracellular,-1000,0
+Exchange,vitd3,Extracellular,-1000,1
+Exchange,ascb_L,Extracellular,-1000,0
+Exchange,retinol,Extracellular,-1000,10
+Exchange,retinal,Extracellular,-1000,10
+Exchange,ala_B,Extracellular,-1000,1
+Exchange,ala_D,Extracellular,-1000,1
 Exchange,h2co3,Extracellular,-1000,1000
 Exchange,h2o2,Extracellular,-1000,1000
 Exchange,hco3,Extracellular,-1000,1000
-Exchange,orn,Extracellular,-1,1000
-Exchange,orn_D,Extracellular,-1,1000
+Exchange,orn,Extracellular,-1000,1
+Exchange,orn_D,Extracellular,-1000,1
 Exchange,so4,Extracellular,-1000,1000
-Exchange,ribflv,Extracellular,-1,1000
-Exchange,Lcystin,Extracellular,-1,1000
\ No newline at end of file
+Exchange,ribflv,Extracellular,-1000,1
+Exchange,Lcystin,Extracellular,-1000,1
diff --git a/main/data/boundary_rxns/smB_boundary_rxns.csv b/main/data/boundary_rxns/smB_boundary_rxns.csv
index 0b08556e..9f0f9385 100644
--- a/main/data/boundary_rxns/smB_boundary_rxns.csv
+++ b/main/data/boundary_rxns/smB_boundary_rxns.csv
@@ -1,4 +1,4 @@
-Boundary,Abbreviation,Compartment,Minimum Reaction Rate,Maximum Reaction Rate
+Reaction,Abbreviation,Compartment,Minimum Reaction Rate,Maximum Reaction Rate
 Exchange,glc_D,Extracellular,-100,1000
 Exchange,fe2,Extracellular,-1000,1000
 Exchange,gal,Extracellular,0,1000
@@ -67,4 +67,4 @@ Exchange,orn,Extracellular,-1,1000
 Exchange,orn_D,Extracellular,-1,1000
 Exchange,so4,Extracellular,-1000,1000
 Exchange,ribflv,Extracellular,-1,1000
-Exchange,Lcystin,Extracellular,-1,1000
\ No newline at end of file
+Exchange,Lcystin,Extracellular,-1,1000

From efbeed189da8d12c3babf875dda40149995692a8 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 5 Sep 2025 11:15:04 -0500
Subject: [PATCH 309/345] feat: provide `identifier_column` for dynamic
 splitting of gene expression data

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/utils.py | 29 ++++++++++++++++-------------
 1 file changed, 16 insertions(+), 13 deletions(-)

diff --git a/main/como/utils.py b/main/como/utils.py
index fc8c9739..3a3d37ce 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -1,14 +1,12 @@
 from __future__ import annotations
 
-import asyncio
 import contextlib
-import functools
 import io
 import sys
 from collections.abc import Iterator
-from concurrent.futures import ThreadPoolExecutor
 from io import TextIOWrapper
 from pathlib import Path
+from typing import Literal
 
 import numpy.typing as npt
 import pandas as pd
@@ -61,30 +59,33 @@ def stringlist_to_list(stringlist: str | list[str]) -> list[str]:
 
 def split_gene_expression_data(
     expression_data: pd.DataFrame,
+    identifier_column: Literal["ensembl_gene_id", "entrez_gene_id"],
     recon_algorithm: Algorithm | None = None,
-    entrez_as_index: bool = True,
+    *,
+    ensembl_as_index: bool = True,
 ):
     """Split the gene expression data into single-gene and multiple-gene names.
 
     :param expression_data: The gene expression data to map
+    :param identifier_column: The column containing the gene identifiers, either 'ensembl_gene_id' or 'entrez_gene_id'
     :param recon_algorithm: The recon algorithm used to generate the gene expression data
-    :param entrez_as_index: Should the 'entrez_gene_id' column be set as the index
+    :param ensembl_as_index: Should the 'ensembl_gene_id' column be set as the index
     :return:
     """
     expression_data.columns = [c.lower() for c in expression_data.columns]
     if recon_algorithm in {Algorithm.IMAT, Algorithm.TINIT}:
         expression_data.rename(columns={"combine_z": "active"}, inplace=True)
 
-    expression_data = expression_data[["entrez_gene_id", "active"]]
-    single_gene_names = expression_data[~expression_data["entrez_gene_id"].astype(str).str.contains("//")]
-    multiple_gene_names = expression_data[expression_data["entrez_gene_id"].astype(str).str.contains("//")]
-    split_gene_names = multiple_gene_names.assign(entrez_gene_id=multiple_gene_names["entrez_gene_id"].astype(str).str.split("///")).explode(
-        "entrez_gene_id"
+    expression_data = expression_data[[identifier_column, "active"]]
+    single_gene_names = expression_data[~expression_data[identifier_column].astype(str).str.contains("//")]
+    multiple_gene_names = expression_data[expression_data[identifier_column].astype(str).str.contains("//")]
+    split_gene_names = multiple_gene_names.assign(ensembl_gene_id=multiple_gene_names[identifier_column].astype(str).str.split("///")).explode(
+        identifier_column
     )
 
     gene_expressions = pd.concat([single_gene_names, split_gene_names], axis=0, ignore_index=True)
-    if entrez_as_index:
-        gene_expressions.set_index("entrez_gene_id", inplace=True)
+    if ensembl_as_index:
+        gene_expressions.set_index(identifier_column, inplace=True)
     return gene_expressions
 
 
@@ -135,6 +136,8 @@ async def _read_file(
         or scanpy.read_h5ad, depending on the filepath provided
     :return: None, or a pandas DataFrame or AnnData
     """
+    if isinstance(path, (pd.DataFrame, sc.AnnData)):
+        return path
     if not path:
         return None
 
@@ -159,7 +162,7 @@ async def _read_file(
             if h5ad_as_df:
                 df = adata.to_df().T
                 df.index.name = "gene_symbol"
-                df.reset_index(inplace=True)
+                df.reset_index(inplace=True, drop=False)
                 return df
             return adata
         case _:

From aac7bbd96e6c42d751010891e41a4747533cbf90 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 5 Sep 2025 11:18:11 -0500
Subject: [PATCH 310/345] feat: use async file reading

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 98 +++++++++++++++++++---------------
 1 file changed, 56 insertions(+), 42 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index b54c96f9..8a843c4b 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import asyncio
+import functools
 import json
 import re
 import sys
@@ -49,17 +50,16 @@ async def build_from_tab(cls, filepath: Path) -> _STARinformation:
                 level=LogLevel.ERROR,
             )
 
-        with filepath.open("r") as i_stream:
-            unmapped = i_stream.readline()
-            multimapping = i_stream.readline()
-            no_feature = i_stream.readline()
-            ambiguous = i_stream.readline()
+        async with aiofiles.open(filepath) as i_stream:
+            unmapped, multimapping, no_feature, ambiguous = await asyncio.gather(
+                *[i_stream.readline(), i_stream.readline(), i_stream.readline(), i_stream.readline()]
+            )
 
             num_unmapped = [int(i) for i in unmapped.rstrip("\n").split("\t")[1:]]
             num_multimapping = [int(i) for i in multimapping.rstrip("\n").split("\t")[1:]]
             num_no_feature = [int(i) for i in no_feature.rstrip("\n").split("\t")[1:]]
             num_ambiguous = [int(i) for i in ambiguous.rstrip("\n").split("\t")[1:]]
-            remainder = await asyncio.to_thread(i_stream.read)
+            remainder = await i_stream.read()
 
         df = await _read_file(StringIO(remainder), sep="\t", header=None)
         df.columns = [
@@ -220,8 +220,9 @@ def _organize_gene_counts_files(data_dir: Path) -> list[_StudyMetrics]:
 
 async def _process_first_multirun_sample(strand_file: Path, all_counts_files: list[Path]):
     sample_count = pd.DataFrame()
-    for file in all_counts_files:
-        star_information = await _STARinformation.build_from_tab(file)
+    all_star_information: list[_STARinformation] = await asyncio.gather(*[_STARinformation.build_from_tab(file) for file in all_counts_files])
+
+    for star_information in all_star_information:
         strand_information = strand_file.read_text().rstrip("\n").lower()
 
         if strand_information not in ("none", "first_read_transcription_strand", "second_read_transcription_strand"):
@@ -238,16 +239,16 @@ async def _process_first_multirun_sample(strand_file: Path, all_counts_files: li
             strand_information = "unstranded_rna_counts"
 
         run_counts = star_information.count_matrix[["ensembl_gene_id", strand_information]]
-        run_counts.columns = pd.Index(["ensembl_gene_id", "counts"])
+        run_counts.columns = ["ensembl_gene_id", "counts"]
         sample_count = run_counts if sample_count.empty else sample_count.merge(run_counts, on=["ensembl_gene_id", "counts"], how="outer")
 
     # Set na values to 0
     sample_count = sample_count.fillna(value="0")
     sample_count["counts"] = sample_count["counts"].astype(np.float64)
 
-    count_sums: pd.DataFrame = pd.DataFrame(sample_count.sum(axis=1, numeric_only=True))
-    count_sums.insert(0, "ensembl_gene_id", sample_count["ensembl_gene_id"])
-    count_sums.columns = pd.Index(["ensembl_gene_id", _sample_name_from_filepath(strand_file)])
+    count_sums = sample_count.groupby("ensembl_gene_id", as_index=False)["counts"].mean()
+    count_sums["counts"] = np.ceil(count_sums["counts"].astype(np.uint32))
+    count_sums.columns = ["ensembl_gene_id", _sample_name_from_filepath(strand_file)]
     return count_sums
 
 
@@ -269,7 +270,7 @@ async def _process_standard_replicate(counts_file: Path, strand_file: Path, samp
         strand_information = "unstranded_rna_counts"
 
     sample_count = star_information.count_matrix[["ensembl_gene_id", strand_information]]
-    sample_count.columns = pd.Index(["ensembl_gene_id", sample_name])
+    sample_count.columns = ["ensembl_gene_id", sample_name]
     return sample_count
 
 
@@ -314,7 +315,8 @@ async def _create_sample_counts_matrix(metrics: _StudyMetrics) -> pd.DataFrame:
         # Remove run number "r\d+" from multi-run names
         if re.search(r"R\d+r1", metrics.sample_names[i]):
             new_sample_name = re.sub(r"r\d+", "", metrics.sample_names[i])
-            counts.columns[i + 1 - adjusted_index] = new_sample_name
+            old_col_name = counts.columns[i + 1 - adjusted_index]
+            counts.rename(columns={old_col_name: new_sample_name}, inplace=True)
 
     return counts
 
@@ -329,12 +331,11 @@ async def _write_counts_matrix(
     """Create a counts matrix file by reading gene counts table(s)."""
     study_metrics = _organize_gene_counts_files(data_dir=como_context_dir)
     counts: list[pd.DataFrame] = await asyncio.gather(*[_create_sample_counts_matrix(metric) for metric in study_metrics])
+    rna_specific_sample_names = set(config_df.loc[config_df["library_prep"] == rna.value, "sample_name"].tolist())
 
-    final_matrix = pd.DataFrame()
-    for count in counts:
-        final_matrix = count if final_matrix.empty else pd.merge(final_matrix, count, on="ensembl_gene_id", how="outer")
-
-    rna_specific_sample_names = config_df.loc[config_df["library_prep"] == rna.value, "sample_name"].tolist()
+    final_matrix: pd.DataFrame = functools.reduce(lambda left, right: pd.merge(left, right, on="ensembl_gene_id", how="outer"), counts)
+    final_matrix.fillna(value=0, inplace=True)
+    final_matrix.iloc[:, 1:] = final_matrix.iloc[:, 1:].astype(np.uint64)
     final_matrix = final_matrix[["ensembl_gene_id", *rna_specific_sample_names]]
 
     output_counts_matrix_filepath.parent.mkdir(parents=True, exist_ok=True)
@@ -418,6 +419,7 @@ async def _create_config_df(  # noqa: C901
 
         fragment_label = f"{context_name}_{label}_fragment_size.txt"
         frag_paths = [p for p in aux_lookup["fragment"].values() if p.name == fragment_label]
+        mean_frag = 100.0
         if not frag_paths and prep != RNAType.TRNA.value:
             logger.warning(f"No fragment file for '{label}'; defaulting to 100 bp (needed for zFPKM).")
             mean_frag = 100.0
@@ -430,6 +432,7 @@ async def _create_config_df(  # noqa: C901
                 counts = np.array([df["frag_count"].sum() for df in dfs])
                 means = np.array([(df["meanxcount"] / df["frag_count"].sum()).sum() for df in dfs])
                 mean_frag = float(np.average(means, weights=counts))
+
         rows.append(
             SampleConfiguration(
                 sample_name=sample_id,
@@ -621,7 +624,6 @@ async def _create_gene_info_file(
     """
 
     async def read_counts(file: Path) -> list[str]:
-        print(f"Will read: '{file}'")
         data = await _read_file(file, h5ad_as_df=False)
 
         try:
@@ -632,7 +634,7 @@ async def read_counts(file: Path) -> list[str]:
             )
         except json.JSONDecodeError:
             _log_and_raise_error(
-                f"Got a JSO decode error for file '{counts_matrix_filepaths}'",
+                f"Got a JSON decode error for file '{counts_matrix_filepaths}'",
                 error=ValueError,
                 level=LogLevel.CRITICAL,
             )
@@ -645,11 +647,12 @@ async def read_counts(file: Path) -> list[str]:
     gene_data = await MyGene(cache=cache).query(items=list(genes), taxon=taxon, scopes="entrezgene")
     gene_info: pd.DataFrame = pd.DataFrame(
         data=None,
-        columns=pd.Index(data=["ensembl_gene_id", "gene_symbol", "entrez_gene_id", "size"]),
-        index=pd.Index(data=range(len(gene_data))),
+        columns=["ensembl_gene_id", "gene_symbol", "entrez_gene_id", "size"],
+        index=list(range(len(gene_data))),
     )
     for i, data in enumerate(gene_data):
-        ensembl_ids = data.get("genomic_pos.ensemblgene", "-")
+        # ensembl_ids = data.get("genomic_pos.ensemblgene", "-")
+        ensembl_ids = data.get("genomic_pos.ensemblgene", pd.NA)
         if isinstance(ensembl_ids, list):
             ensembl_ids = ensembl_ids[0]
 
@@ -658,14 +661,16 @@ async def read_counts(file: Path) -> list[str]:
         end_pos = data.get("genomic_pos.end", 0)
         end_pos: int = int(sum(end_pos) / len(end_pos)) if isinstance(end_pos, list) else int(end_pos)
 
-        gene_info.at[i, "gene_symbol"] = data.get("symbol", "-")
-        gene_info.at[i, "entrez_gene_id"] = data.get("entrezgene", "-")
+        gene_info.at[i, "gene_symbol"] = data.get("symbol", pd.NA)
+        gene_info.at[i, "entrez_gene_id"] = data.get("entrezgene", pd.NA)
         gene_info.at[i, "ensembl_gene_id"] = ensembl_ids
         gene_info.at[i, "size"] = end_pos - start_pos
 
-    gene_info = gene_info[((gene_info["entrez_gene_id"] != "-") & (gene_info["ensembl_gene_id"] != "-") & (gene_info["gene_symbol"] != "-"))]
+    gene_info = gene_info[((~gene_info["entrez_gene_id"].isna()) & (~gene_info["ensembl_gene_id"].isna()) & (~gene_info["gene_symbol"].isna()))]
     gene_info.sort_values(by="ensembl_gene_id", inplace=True)
     gene_info.dropna(inplace=True)
+
+    output_filepath.parent.mkdir(parents=True, exist_ok=True)
     gene_info.to_csv(output_filepath, index=False)
     logger.success(f"Gene Info file written at '{output_filepath}'")
 
@@ -700,7 +705,9 @@ async def _process(
     output_mrna_config_filepath: Path | None,
     output_trna_matrix_filepath: Path | None,
     output_mrna_matrix_filepath: Path | None,
+    *,
     cache: bool,
+    create_gene_info_only: bool,
 ):
     rna_types: list[tuple[RNAType, Path, Path]] = []
     if output_trna_config_filepath:
@@ -709,29 +716,31 @@ async def _process(
         rna_types.append((RNAType.MRNA, output_mrna_config_filepath, output_mrna_matrix_filepath))
 
     # if provided, iterate through como-input specific directories
-    tasks = []
-    for rna, output_config_filepath, output_matrix_filepath in rna_types:
-        tasks.append(
-            asyncio.create_task(
-                _process_como_input(
-                    context_name=context_name,
-                    output_config_filepath=output_config_filepath,
-                    como_context_dir=como_context_dir,
-                    output_counts_matrix_filepath=output_matrix_filepath,
-                    rna=rna,
+    if not create_gene_info_only:
+        tasks = []
+        for rna, output_config_filepath, output_matrix_filepath in rna_types:
+            tasks.append(
+                asyncio.create_task(
+                    _process_como_input(
+                        context_name=context_name,
+                        output_config_filepath=output_config_filepath,
+                        como_context_dir=como_context_dir,
+                        output_counts_matrix_filepath=output_matrix_filepath,
+                        rna=rna,
+                    )
                 )
             )
-        )
 
-    await asyncio.gather(*tasks)
+        await asyncio.gather(*tasks)
 
     # create the gene info filepath based on provided data
-
     input_files = []
     if input_matrix_filepath:
         input_files.extend(input_matrix_filepath)
-    output_trna_matrix_filepath and input_files.append(output_trna_matrix_filepath)
-    output_mrna_matrix_filepath and input_files.append(output_mrna_matrix_filepath)
+    if output_trna_matrix_filepath:
+        input_files.append(output_trna_matrix_filepath)
+    if output_mrna_matrix_filepath:
+        input_files.append(output_mrna_matrix_filepath)
 
     await _create_gene_info_file(
         counts_matrix_filepaths=input_files,
@@ -754,6 +763,8 @@ async def rnaseq_preprocess(
     cache: bool = True,
     log_level: LogLevel | str = LogLevel.INFO,
     log_location: str | TextIOWrapper = sys.stderr,
+    *,
+    create_gene_info_only: bool = False,
 ) -> None:
     """Preprocesses RNA-seq data for downstream analysis.
 
@@ -773,6 +784,7 @@ async def rnaseq_preprocess(
     :param cache: Should HTTP requests be cached
     :param log_level: The logging level
     :param log_location: The logging location
+    :param create_gene_info_only: If True, only create the gene info file and skip general preprocessing steps
     """
     _set_up_logging(level=log_level, location=log_location)
 
@@ -780,6 +792,7 @@ async def rnaseq_preprocess(
 
     if como_context_dir:
         como_context_dir = como_context_dir.resolve()
+
     input_matrix_filepath = [i.resolve() for i in _listify(input_matrix_filepath)] if input_matrix_filepath else None
     output_trna_metadata_filepath = output_trna_metadata_filepath.resolve() if output_trna_metadata_filepath else None
     output_mrna_metadata_filepath = output_mrna_metadata_filepath.resolve() if output_mrna_metadata_filepath else None
@@ -797,6 +810,7 @@ async def rnaseq_preprocess(
         output_trna_matrix_filepath=output_trna_count_matrix_filepath,
         output_mrna_matrix_filepath=output_mrna_count_matrix_filepath,
         cache=cache,
+        create_gene_info_only=create_gene_info_only,
     )
 
 

From d1dae99ee9a41f1e2286f2a21c8555030ac5bfdc Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 5 Sep 2025 11:18:48 -0500
Subject: [PATCH 311/345] refactor!: rename `graph` to `plot`

BREAKING-CHANGE: use `como.plot` instead of `como.graph`
Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/__init__.py       |  1 +
 main/como/graph/__init__.py | 50 -------------------------------------
 main/como/plot/__init__.py  |  0
 3 files changed, 1 insertion(+), 50 deletions(-)
 delete mode 100644 main/como/graph/__init__.py
 create mode 100644 main/como/plot/__init__.py

diff --git a/main/como/__init__.py b/main/como/__init__.py
index c84ef56e..cba3d35f 100644
--- a/main/como/__init__.py
+++ b/main/como/__init__.py
@@ -1,3 +1,4 @@
+from como import plot
 from como.data_types import AdjustmentMethod, Algorithm, CobraCompartments, FilteringTechnique, LogLevel, Solver
 from como.utils import stringlist_to_list
 
diff --git a/main/como/graph/__init__.py b/main/como/graph/__init__.py
deleted file mode 100644
index 7594db2f..00000000
--- a/main/como/graph/__init__.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from pathlib import Path
-
-import pandas as pd
-import seaborn as sns
-from loguru import logger
-from matplotlib import pyplot as plt
-
-
-def z_score_distribution(
-    df: pd.DataFrame,
-    title: str,
-    output_filepath: Path,
-):
-    """Graph a z-score distribution.
-
-    :param df: The z-score data to graph
-    :param title: Title to add to graph
-    :param output_filepath: Output PNG filepath location
-    :return: None
-    """
-    if output_filepath.suffix not in {".png", ".pdf", ".svg"}:
-        logger.warning(
-            f"Expected .png, .pdf, or .svg suffix for output_png_filepath, got {output_filepath.suffix}. "
-            f"Defaulting to .pdf"
-        )
-        output_filepath = output_filepath.with_suffix(".pdf")
-    logger.trace("Graphing z-score distribution")
-    output_filepath.parent.mkdir(parents=True, exist_ok=True)
-    output_filepath.unlink(missing_ok=True)
-
-    plt.figure(figsize=(10, 6))
-
-    if len(df["source"].unique()) == 1:
-        ax = sns.histplot(df, x="zscore", bins=100, kde=True)
-        sns.rugplot(df, x="zscore", ax=ax)
-    else:
-        sns.histplot(df, x="zscore", hue="source", bins=100, kde=True, element="step")
-        plt.legend(loc="upper right", frameon=False, title=None)
-
-    plt.title(title)
-    plt.xlabel("Z-score")
-    plt.ylabel("Frequency")
-    plt.xticks(fontsize=12)
-    plt.yticks(fontsize=12)
-    plt.gca().spines["top"].set_visible(False)
-    plt.gca().spines["right"].set_visible(False)
-    plt.tight_layout()
-    plt.savefig(output_filepath)
-    plt.close()
-    logger.success(f"Saved z-score distribution graph to '{output_filepath}'")
diff --git a/main/como/plot/__init__.py b/main/como/plot/__init__.py
new file mode 100644
index 00000000..e69de29b

From b7983c3b95b39ee29af64a61cbf1ed9d2f06f475 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 5 Sep 2025 11:19:22 -0500
Subject: [PATCH 312/345] chore: remove trailing underscore; do not write index

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/combine_distributions.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 9717eca1..5311fd9b 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -225,7 +225,7 @@ async def _begin_combining_distributions(
                     matrix=matrix[[GeneIdentifier.ENSEMBL_GENE_ID.value, *batch.sample_names]],
                     source=source,
                     output_combined_matrix_filepath=(
-                        output_filepaths[source.value].parent / f"{context_name}_{source.value}_batch{batch.batch_num}_combined_z_distribution_.csv"
+                        output_filepaths[source.value].parent / f"{context_name}_{source.value}_batch{batch.batch_num}_combined_z_distribution.csv"
                     ),
                     output_figure_dirpath=output_figure_dirpath,
                     weighted_z_floor=weighted_z_floor,
@@ -257,7 +257,7 @@ async def _begin_combining_distributions(
                 weight=source_weights[source.value],
             )
         )
-        merged_source_results.to_csv(output_filepaths[source.value])
+        merged_source_results.to_csv(output_filepaths[source.value], index=False)
         logger.success(f"Wrote z-scores for source '{source.value}' in context '{context_name}' to '{output_filepaths[source.value]}'")
 
     logger.trace(f"Combining z-score distributions for all sources in context '{context_name}'")

From 009fbb23c6b7d38badf4f1d1d02d459d6976f4ba Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 5 Sep 2025 11:20:31 -0500
Subject: [PATCH 313/345] feat: add identifier column check for gene expression
 data processing

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/create_context_specific_model.py | 20 ++++++++++++++++----
 1 file changed, 16 insertions(+), 4 deletions(-)

diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 8818280e..6ac2327e 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -6,6 +6,7 @@
 from collections.abc import Sequence
 from io import TextIOWrapper
 from pathlib import Path
+from typing import Literal, cast
 
 import cobra
 import numpy as np
@@ -263,7 +264,18 @@ async def _map_expression_to_reaction(
 
     These rules should be defined in the general genome-scale metabolic model
     """
-    gene_activity = split_gene_expression_data(await _read_file(gene_expression_file), recon_algorithm=recon_algorithm)
+    expression_data = await _read_file(gene_expression_file)
+    identifier_column = next((col for col in ("entrez_gene_id", "ensembl_gene_id") if col in expression_data.columns), "")
+
+    if not identifier_column:
+        raise ValueError(
+            f"At least one column of 'entrez_gene_id' or 'ensembl_gene_id' could not be found in the gene expression file '{gene_expression_file}'"
+        )
+    gene_activity = split_gene_expression_data(
+        expression_data,
+        identifier_column=cast(Literal["ensembl_gene_id", "entrez_gene_id"], identifier_column),
+        recon_algorithm=recon_algorithm,
+    )
     reaction_expression = collections.OrderedDict()
 
     # fmt: off
@@ -281,19 +293,19 @@ async def _map_expression_to_reaction(
         rxn: cobra.Reaction
 
         gene_reaction_rule = rxn.gene_reaction_rule
-        if gene_reaction_rule == "":
+        if not gene_reaction_rule:
             continue
 
         gene_ids = set(re.findall(r"\d+", gene_reaction_rule))
         reaction_expression[rxn.id] = default_expression
         for gene_id in gene_ids:
-            activity = f"{gene_activity.at[gene_id, 'active']}" if gene_id in gene_activity.index else f"{default_expression!s}"
+            activity = gene_activity.at[gene_id, "active"] if gene_id in gene_activity.index else f"{default_expression!s}"
             # replace gene_id with activity, using optional whitespace before and after the gene id
             # Do not replace the whitespace (if it exists) before and after the gene ID
             gene_reaction_rule = re.sub(pattern=rf"\b{gene_id}\b", repl=activity, string=gene_reaction_rule)
 
         try:
-            # We are using eval here because ast.literal_eval is unable to process an evaluable such as `max(-4, -4)`
+            # We are using eval here because ast.literal_eval is unable to process an evaluable such as `max(-4, 0, 1)`
             # This isn't ideal, but ultimately the only other option is writing and maintaining a custom parsing engine, which is too much work
             evaluable_gene_rule = _gene_rule_logical(gene_reaction_rule).replace("{", "(").replace("}", ")")
             reaction_expression[rxn.id] = eval(evaluable_gene_rule)  # noqa: S307

From 0a7c0a60b0206059c81c3a22024181ae1edaa4f0 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 5 Sep 2025 11:20:50 -0500
Subject: [PATCH 314/345] fix: replace '-' with pd.NA in drug target genes
 DataFrame

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/knock_out_simulation.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/main/como/knock_out_simulation.py b/main/como/knock_out_simulation.py
index fe96c784..9e4e578a 100644
--- a/main/como/knock_out_simulation.py
+++ b/main/como/knock_out_simulation.py
@@ -74,7 +74,7 @@ def knock_out_simulation(
         drug_db = drug_db[drug_db["moa"].str.lower().str.contains("inhibitor")]
         drug_target_genes = pd.DataFrame(columns=["Gene ID"])
         drug_target_genes["Gene ID"] = drug_db["Gene ID"].astype(str)
-        drug_target_genes.replace("-", np.nan, inplace=True)
+        drug_target_genes.replace("-", pd.NA, inplace=True)
         drug_target_genes.dropna(axis=0, inplace=True)
         drug_target_genes.to_csv(inhibitors_filepath, header=True, sep="\t", index=False)
         print(f"Inhibitors file written to: {inhibitors_filepath}")
@@ -280,8 +280,7 @@ def main(argv):
     parser = argparse.ArgumentParser(
         prog="knock_out_simulation.py",
         description="This script is responsible for mapping drug targets in metabolic models, performing knock out simulations, and comparing simulation results with disease genes. It also identified drug targets and repurposable drugs.",
-        epilog="For additional help, please post questions/issues in the MADRID GitHub repo at "
-        "https://github.com/HelikarLab/COMO",
+        epilog="For additional help, please post questions/issues in the MADRID GitHub repo at https://github.com/HelikarLab/COMO",
     )
     parser.add_argument(
         "-m",

From 3468625d84cd97fb1c42394418b75c3f03211e6e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 5 Sep 2025 11:21:49 -0500
Subject: [PATCH 315/345] style: ruff formatting

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/merge_xomics.py | 36 +++++++++++++-----------------------
 1 file changed, 13 insertions(+), 23 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index a25f8e78..b613b92a 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -110,9 +110,7 @@ def _merge_logical_table(df: pd.DataFrame):
     df.dropna(subset=["entrez_gene_id"], inplace=True)
     df["entrez_gene_id"] = df["entrez_gene_id"].astype(str).str.replace(" /// ", "//").astype(str)
 
-    id_list: list[str] = df.loc[
-        ~df["entrez_gene_id"].str.contains("//"), "entrez_gene_id"
-    ].tolist()  # Collect "single" ids, like "123"
+    id_list: list[str] = df.loc[~df["entrez_gene_id"].str.contains("//"), "entrez_gene_id"].tolist()  # Collect "single" ids, like "123"
     multiple_entrez_ids: list[str] = df.loc[
         df["entrez_gene_id"].str.contains("//"), "entrez_gene_id"
     ].tolist()  # Collect "double" ids, like "123//456"
@@ -291,9 +289,7 @@ async def _merge_xomics(
 
     logger.trace(f"Shape of merged data before merging logical tables: {merge_data.shape}")
     if merge_data.empty:
-        logger.warning(
-            f"No data is available for the '{context_name}' context. If this is intentional, ignore this error."
-        )
+        logger.warning(f"No data is available for the '{context_name}' context. If this is intentional, ignore this error.")
         return {}
 
     merge_data = _merge_logical_table(merge_data)
@@ -306,9 +302,7 @@ async def _merge_xomics(
     if adjust_for_missing_sources:  # Subtract 1 from requirement per missing source
         logger.trace("Adjusting for missing data sources")
         merge_data.loc[:, "required"] = merge_data[expression_list].apply(
-            lambda x: expression_requirement - (num_sources - x.count())
-            if (expression_requirement - (num_sources - x.count()) > 0)
-            else 1,
+            lambda x: expression_requirement - (num_sources - x.count()) if (expression_requirement - (num_sources - x.count()) > 0) else 1,
             axis=1,
         )
     else:  # Do not adjust for missing sources
@@ -354,10 +348,10 @@ async def _update_missing_data(input_matrices: _InputMatrices, taxon_id: int) ->
     results = await asyncio.gather(
         *[
             # Using 'is not None' is required because the truth value of a Dataframe is ambiguous
-            get_missing_gene_data(values=input_matrices.trna, taxon_id=taxon_id) if input_matrices.trna is not None else asyncio.sleep(0),  # noqa: E501
-            get_missing_gene_data(values=input_matrices.mrna, taxon_id=taxon_id) if input_matrices.mrna is not None else asyncio.sleep(0),  # noqa: E501
-            get_missing_gene_data(values=input_matrices.scrna, taxon_id=taxon_id) if input_matrices.scrna is not None else asyncio.sleep(0),  # noqa: E501
-            get_missing_gene_data(values=input_matrices.proteomics, taxon_id=taxon_id) if input_matrices.proteomics is not None else asyncio.sleep(0),  # noqa: E501
+            get_missing_gene_data(values=input_matrices.trna, taxon_id=taxon_id) if input_matrices.trna is not None else asyncio.sleep(0),
+            get_missing_gene_data(values=input_matrices.mrna, taxon_id=taxon_id) if input_matrices.mrna is not None else asyncio.sleep(0),
+            get_missing_gene_data(values=input_matrices.scrna, taxon_id=taxon_id) if input_matrices.scrna is not None else asyncio.sleep(0),
+            get_missing_gene_data(values=input_matrices.proteomics, taxon_id=taxon_id) if input_matrices.proteomics is not None else asyncio.sleep(0),
         ]
     )
     # fmt: on
@@ -376,10 +370,7 @@ async def _update_missing_data(input_matrices: _InputMatrices, taxon_id: int) ->
             # fmt: on
             logger.trace(f"Merging conversion data for {matrix_name}, existing id column is: {existing_data}")
             input_matrices[matrix_name] = (
-                input_matrices[matrix_name]
-                .merge(conversion, how="left", on=[existing_data])
-                .dropna()
-                .reset_index(drop=True)
+                input_matrices[matrix_name].merge(conversion, how="left", on=[existing_data]).dropna().reset_index(drop=True)
             )
 
     logger.debug("Updated missing genomic data")
@@ -577,8 +568,8 @@ async def merge_xomics(  # noqa: C901
 
     # fmt: off
     source_data = {
-        SourceTypes.TRNA: (trna_matrix_or_filepath, trna_boolean_matrix_or_filepath, trna_metadata_filepath_or_df, output_trna_activity_filepath),  # noqa: E501
-        SourceTypes.MRNA: (mrna_matrix_or_filepath, mrna_boolean_matrix_or_filepath, mrna_metadata_filepath_or_df, output_mrna_activity_filepath),  # noqa: E501
+        SourceTypes.TRNA: (trna_matrix_or_filepath, trna_boolean_matrix_or_filepath, trna_metadata_filepath_or_df, output_trna_activity_filepath),
+        SourceTypes.MRNA: (mrna_matrix_or_filepath, mrna_boolean_matrix_or_filepath, mrna_metadata_filepath_or_df, output_mrna_activity_filepath),
         SourceTypes.SCRNA: (scrna_matrix_or_filepath, scrna_boolean_matrix_or_filepath, scrna_metadata_filepath_or_df, output_scrna_activity_filepath),  # noqa: E501
         SourceTypes.PROTEOMICS: (proteomic_matrix_or_filepath, proteomic_boolean_matrix_or_filepath, proteomic_metadata_filepath_or_df, output_proteomic_activity_filepath),  # noqa: E501
     }
@@ -604,10 +595,8 @@ async def merge_xomics(  # noqa: C901
             level=LogLevel.ERROR,
         )
 
-    if expression_requirement < 1:
-        logger.warning(
-            f"Expression requirement must be at least 1! Setting to the minimum of 1 now. Got: {expression_requirement}"
-        )
+    if not expression_requirement or expression_requirement < 1:
+        logger.warning(f"Expression requirement must be at least 1! Setting to the minimum of 1 now. Got: {expression_requirement}")
         expression_requirement = 1
 
     if expression_requirement is None:
@@ -663,6 +652,7 @@ async def merge_xomics(  # noqa: C901
     )
 
     # build scrna items
+
     scrna_matrix: pd.DataFrame | None
     scrna_boolean_matrix: pd.DataFrame | None
     scrna_metadata: pd.DataFrame | None

From 6561c24a7aee528da65cc7e106ce5f5b8f6bdee0 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 5 Sep 2025 11:22:12 -0500
Subject: [PATCH 316/345] refactor: use pd.NA instead of checking "-"

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/proteomics_gen.py | 11 +++--------
 1 file changed, 3 insertions(+), 8 deletions(-)

diff --git a/main/como/proteomics_gen.py b/main/como/proteomics_gen.py
index dd50a75d..102490ba 100644
--- a/main/como/proteomics_gen.py
+++ b/main/como/proteomics_gen.py
@@ -45,7 +45,7 @@ async def load_gene_symbol_map(gene_symbols: list[str], entrez_map: Path | None
             input_db=Input.GENE_SYMBOL,
             output_db=[Output.GENE_ID, Output.ENSEMBL_GENE_ID],
         )
-        df.loc[df["gene_id"] == "-", ["gene_id"]] = np.nan
+        df.loc[df["gene_id"].isna(), ["gene_id"]] = np.nan
         df.to_csv(entrez_map, index_label="gene_symbol")
 
     return df[~df.index.duplicated()]
@@ -134,9 +134,7 @@ def load_empty_dict():
 
     inquiry_full_path = config.data_dir / "config_sheets" / filename
     if not inquiry_full_path.exists():
-        _log_and_raise_error(
-            f"Error: file not found {inquiry_full_path}", error=FileNotFoundError, level=LogLevel.ERROR
-        )
+        _log_and_raise_error(f"Error: file not found {inquiry_full_path}", error=FileNotFoundError, level=LogLevel.ERROR)
 
     filename = f"Proteomics_{context_name}.csv"
     full_save_filepath = config.result_dir / context_name / "proteomics" / filename
@@ -146,10 +144,7 @@ def load_empty_dict():
         return context_name, data
 
     else:
-        logger.warning(
-            f"Proteomics gene expression file for {context_name} was not found at {full_save_filepath}. "
-            f"Is this intentional?"
-        )
+        logger.warning(f"Proteomics gene expression file for {context_name} was not found at {full_save_filepath}. Is this intentional?")
         return load_empty_dict()
 
 

From bf5606943a382ab8c966745ff4e97a317f3584ef Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 5 Sep 2025 11:22:36 -0500
Subject: [PATCH 317/345] fix: only drop NA values from ensembl_gene_id, not
 entire dataframe

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 33 +++++++++++++++++++++++++--------
 1 file changed, 25 insertions(+), 8 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index e2e988d4..d4ba7335 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -159,7 +159,7 @@ async def _build_matrix_results(
     :param taxon: The NCBI Taxon ID
     :return: A dataclass `ReadMatrixResults`
     """
-    matrix.dropna(inplace=True)
+    matrix.dropna(subset="ensembl_gene_id", inplace=True)
     conversion = await ensembl_to_gene_id_and_symbol(ids=matrix["ensembl_gene_id"].tolist(), taxon=taxon)
     conversion["ensembl_gene_id"] = conversion["ensembl_gene_id"].str.split(",")
     conversion = conversion.explode("ensembl_gene_id")
@@ -184,11 +184,12 @@ async def _build_matrix_results(
     # Only include Entrez and Ensembl Gene IDs that are present in `gene_info`
     matrix["entrez_gene_id"] = matrix["entrez_gene_id"].str.split("//")
     matrix = matrix.explode("entrez_gene_id")
-    matrix = matrix.replace(to_replace="-", value=pd.NA).dropna()
+    # matrix = matrix.replace(to_replace="-", value=pd.NA)
+    matrix.dropna(subset="entrez_gene_id", inplace=True)
     matrix["entrez_gene_id"] = matrix["entrez_gene_id"].astype(int)
 
     gene_info = gene_info_migrations(gene_info)
-    gene_info = gene_info.replace(to_replace="-", value=pd.NA).dropna()
+    # gene_info = gene_info.replace(to_replace="-", value=pd.NA)
     gene_info["entrez_gene_id"] = gene_info["entrez_gene_id"].astype(int)
 
     counts_matrix = matrix.merge(
@@ -244,9 +245,10 @@ def _calculate_fpkm(metrics: NamedMetrics, scale: int = 1e6) -> NamedMetrics:
     """Calculate the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) for each sample in the metrics dictionary."""
     for study in metrics:
         matrix_values = []
+
         for sample in range(metrics[study].num_samples):
             layout = metrics[study].layout[sample]
-            count_matrix: pd.DataFrame = metrics[study].count_matrix.iloc[:, sample].values.astype(np.float32)
+            count_matrix: npt.NDArray[np.float32] = metrics[study].count_matrix.iloc[:, sample].values
             gene_lengths = (
                 metrics[study].fragment_lengths[sample].astype(np.float32)
                 if layout == LayoutMethod.paired_end
@@ -302,6 +304,7 @@ def _zfpkm_calculation(
     Stabilize the variance in the data to make the distribution more symmetric; this is helpful for Gaussian fitting
 
     Kernel Density Estimation (kde)
+        - SciKit Learn: https://scikit-learn.org/stable/modules/density.html
         - Non-parametric method to estimate the probability density function (PDF) of a random variable
         - Estimates the distribution of log2-transformed FPKM values
         - Bandwidth parameter controls the smoothness of the density estimate
@@ -340,8 +343,11 @@ def _zfpkm_calculation(
             a threshold for calling a gene as "active" and/or "expressed"
             : https://doi.org/10.1186/1471-2164-14-778
     """
-    values = column.values
-    refit: KernelDensity = KernelDensity(kernel="gaussian", bandwidth=bandwidth).fit(values.reshape(-1, 1))  # type: ignore
+    values: npt.NDArray = column.values
+    # replace na values with 0
+    values = np.nan_to_num(values, nan=0.0)
+    refit: KernelDensity = KernelDensity(kernel="gaussian", bandwidth=bandwidth)
+    refit.fit(values.reshape(-1, 1))
 
     x_range = np.linspace(values.min(), values.max(), 2000)
     density = np.exp(refit.score_samples(x_range.reshape(-1, 1)))
@@ -702,9 +708,9 @@ async def _process(
     rnaseq_matrix: pd.DataFrame = await _read_file(rnaseq_matrix_filepath)
     if rnaseq_matrix_filepath.suffix == ".h5ad":
         conversion = await gene_symbol_to_ensembl_and_gene_id(symbols=rnaseq_matrix["gene_symbol"].tolist(), taxon=taxon)
-        conversion.reset_index(inplace=True)
+        conversion.reset_index(inplace=True, drop=False)
         rnaseq_matrix = rnaseq_matrix.merge(conversion, how="left", on="gene_symbol")
-        rnaseq_matrix.replace(to_replace=pd.NA, value="-")
+        # rnaseq_matrix = rnaseq_matrix.replace(to_replace=pd.NA, value="-")
 
     filtering_options = _FilteringOptions(
         replicate_ratio=replicate_ratio,
@@ -916,6 +922,17 @@ async def rnaseq_gen(  # noqa: C901
             level=LogLevel.ERROR,
         )
 
+    metadata_df["fragment_length"] = metadata_df["fragment_length"].astype(np.float32)
+    metadata_df = metadata_df.groupby("sample_name", as_index=False).agg(
+        {
+            "sample_name": "first",
+            "fragment_length": "mean",
+            "layout": "first",
+            "strand": "first",
+            "study": "first",
+            "library_prep": "first",
+        }
+    )
     logger.debug(f"Starting '{context_name}'")
     await _process(
         context_name=context_name,

From dc324d5d4121c6e40fdb70c9fee115a367086857 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 5 Sep 2025 11:23:00 -0500
Subject: [PATCH 318/345] feat: added function to build heatmap of conditions
 vs pathways

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/plot/heatmap.py | 101 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 101 insertions(+)
 create mode 100644 main/como/plot/heatmap.py

diff --git a/main/como/plot/heatmap.py b/main/como/plot/heatmap.py
new file mode 100644
index 00000000..cfa21c64
--- /dev/null
+++ b/main/como/plot/heatmap.py
@@ -0,0 +1,101 @@
+from __future__ import annotations
+
+import math
+from pathlib import Path
+
+import matplotlib.colors as mcolors
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from matplotlib.ticker import FixedLocator
+
+
+def condition_vs_pathway(
+    data: pd.DataFrame,
+    save_filepath: Path | None = None,
+    *,
+    copy_df: bool = False,
+    exclude_zero_flux_pathways: bool = False,
+) -> plt.Figure:
+    """Build a heatmap of fluxes through pathways across conditions.
+
+    :param data: Index values are conditions, column names are pathways
+    :param save_filepath: If provided, the resulting figure will be saved to this location
+    :param copy_df: Should the incoming dataframe be copied to prevent modifications to data?
+    :param exclude_zero_flux_pathways: Should pathways that have 0 flux across all rows be excluded?
+
+    :return: The resulting `matpotlib.pyplt.Figure` object
+    """
+    plot_df: pd.DataFrame = data.copy() if copy_df else data
+    plot_df = plot_df.astype(np.float32)
+    fig = plt.figure(figsize=(100, 40), dpi=175)
+
+    if exclude_zero_flux_pathways:
+        # Select pathways that have at least one non-zero value
+        plot_df = plot_df.loc[:, plot_df.where(plot_df != 0).any(axis=0)]
+
+    # Identify the second largest (pos or neg) value
+    # This is needed in order to set the upper & lower bounds for the graph, excluding +/- 1000-flux values
+    plot_df[plot_df > 1000] = 1001
+    plot_df[plot_df < -1000] = -1001
+    second_largest_positive = plot_df[plot_df > 0].stack().drop_duplicates().nlargest(2).iloc[-1]
+    second_largest_negative = plot_df[plot_df < 0].stack().drop_duplicates().nsmallest(2).iloc[-1]
+    vmax = max(abs(second_largest_negative), second_largest_positive)
+
+    # Convert tick marks to reasonable values:
+    #   max tick < 100: round to 10s place
+    #   max tick < 1_000: round to 100s place
+    #   max tick < 10_000: round to 1_000s place
+    base = 10 if vmax < 100 else 100 if vmax < 10_000 else 1000
+    vmax_root = math.ceil(vmax / base) * base
+
+    # Create 5 evenly spaced ticks along the legend
+    ticks = np.linspace(-vmax_root, vmax_root, 5)
+
+    # Generate legend gradient
+    norm = mcolors.TwoSlopeNorm(vmin=-vmax_root, vcenter=0, vmax=vmax_root)
+
+    # If a value falls outside of `vmax_root`, set it to the following colors
+    cmap = plt.get_cmap("coolwarm").copy()
+    cmap.set_over("#660033")
+    cmap.set_under("#000099")
+
+    ax: plt.Axes = sns.heatmap(
+        data=plot_df,
+        linewidths=1.0,
+        linecolor="#686868",
+        center=0,
+        yticklabels=True,
+        xticklabels=True,
+        norm=norm,
+        cmap=cmap,
+        cbar_kws={"extend": "both", "label": f"Flux ratio (clipped at ±{vmax:.0f})"},
+    )
+
+    plt.title("Metabolic Model Flux Sum through Pathways", fontsize=100)
+
+    plt.xlabel("Pathway", fontsize=80)
+    ax.tick_params(axis="x", which="major", labelsize=55, labelrotation=90)
+
+    plt.ylabel("Condition", fontsize=85)
+    ax.tick_params(axis="y", which="major", labelsize=55, labelrotation=0)
+
+    cbar = ax.collections[0].colorbar
+    cbar.set_ticks(ticks)
+    cbar.ax.yaxis.set_major_locator(FixedLocator(ticks))
+    cbar.update_ticks()
+
+    # Add extended triangles that are detached from the colorbar (prevents very large pos/neg values from blowing out the legend)
+    cbar.ax.text(0.5, 1.06, "> +1000", ha="center", va="bottom", transform=cbar.ax.transAxes, fontsize=40)
+    cbar.ax.text(0.5, -0.06, "< -1000", ha="center", va="top", transform=cbar.ax.transAxes, fontsize=40)
+    cbar.ax.tick_params(labelsize=40)
+    cbar.set_label("Flux", rotation=270, labelpad=40)
+
+    fig.tight_layout(h_pad=0.85)
+
+    if save_filepath:
+        save_filepath.parent.mkdir(parents=True, exist_ok=True)
+        fig.savefig(save_filepath, transparent=False, bbox_inches="tight")
+
+    return fig

From 74d883db1fd49fa63ff60545d3911ea7ba7e0f38 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 5 Sep 2025 11:23:16 -0500
Subject: [PATCH 319/345] feat: added function to plot z score distribution

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/plot/z_score.py | 47 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 47 insertions(+)
 create mode 100644 main/como/plot/z_score.py

diff --git a/main/como/plot/z_score.py b/main/como/plot/z_score.py
new file mode 100644
index 00000000..0822e63a
--- /dev/null
+++ b/main/como/plot/z_score.py
@@ -0,0 +1,47 @@
+from pathlib import Path
+
+import pandas as pd
+import seaborn as sns
+from loguru import logger
+from matplotlib import pyplot as plt
+
+
+def z_score_distribution(
+    df: pd.DataFrame,
+    title: str,
+    output_filepath: Path,
+):
+    """Graph a z-score distribution.
+
+    :param df: The z-score data to graph
+    :param title: Title to add to graph
+    :param output_filepath: Output PNG filepath location
+    :return: None
+    """
+    if output_filepath.suffix not in {".png", ".pdf", ".svg"}:
+        logger.warning(f"Expected .png, .pdf, or .svg suffix for output_png_filepath, got {output_filepath.suffix}. Defaulting to .pdf")
+        output_filepath = output_filepath.with_suffix(".pdf")
+    logger.trace("Graphing z-score distribution")
+    output_filepath.parent.mkdir(parents=True, exist_ok=True)
+    output_filepath.unlink(missing_ok=True)
+
+    plt.figure(figsize=(10, 6))
+
+    if len(df["source"].unique()) == 1:
+        ax = sns.histplot(df, x="zscore", bins=100, kde=True)
+        sns.rugplot(df, x="zscore", ax=ax)
+    else:
+        sns.histplot(df, x="zscore", hue="source", bins=100, kde=True, element="step")
+        plt.legend(loc="upper right", frameon=False, title=None)
+
+    plt.title(title)
+    plt.xlabel("Z-score")
+    plt.ylabel("Frequency")
+    plt.xticks(fontsize=12)
+    plt.yticks(fontsize=12)
+    plt.gca().spines["top"].set_visible(False)
+    plt.gca().spines["right"].set_visible(False)
+    plt.tight_layout()
+    plt.savefig(output_filepath)
+    plt.close()
+    logger.success(f"Saved z-score distribution graph to '{output_filepath}'")

From b55215d21483aa4854385506b9679be3006b62ea Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 5 Sep 2025 11:24:03 -0500
Subject: [PATCH 320/345] feat: added `pipelines` module to make using
 utilities easier

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/pipelines/__init__.py               |   0
 .../pipelines/build_condition_heatmaps.py     | 290 ++++++++++++++++++
 2 files changed, 290 insertions(+)
 create mode 100644 main/como/pipelines/__init__.py
 create mode 100644 main/como/pipelines/build_condition_heatmaps.py

diff --git a/main/como/pipelines/__init__.py b/main/como/pipelines/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/main/como/pipelines/build_condition_heatmaps.py b/main/como/pipelines/build_condition_heatmaps.py
new file mode 100644
index 00000000..a0161a45
--- /dev/null
+++ b/main/como/pipelines/build_condition_heatmaps.py
@@ -0,0 +1,290 @@
+"""Create heatmaps of conditions vs pathway flux.
+
+This pipeline will generate heatmaps showing the flux through various pathways
+"""
+
+from __future__ import annotations
+
+import concurrent.futures
+from collections import defaultdict
+from functools import partial
+from pathlib import Path
+
+import aiofiles.os
+import aiofiles.ospath
+import cobra
+import matplotlib.pyplot as plt
+import numpy as np
+import numpy.typing as npt
+import orjson
+import pandas as pd
+from pandas._libs.missing import NAType
+
+from como.plot.heatmap import condition_vs_pathway
+
+
+async def afind_possible_model_filepaths(search_dir: Path) -> list[Path]:
+    """Asynchronously find potential files that could be constraint-based metabolic models.
+
+    :param search_dir: The directory to search for models.
+    :return: Potential filepaths that could be loaded as a `cobra.Model` object
+    """
+    return [f for f in list(await asyncio.to_thread(search_dir.rglob, pattern="*")) if f.suffix in {".mat", ".json", ".sbml", ".xml"}]
+
+
+def find_possible_model_filepaths(search_dir: Path) -> list[Path]:
+    """Find potential files that could be constraint-based metabolic models.
+
+    :param search_dir: The directory to search for models.
+    :return: Potential filepaths that could be loaded as a `cobra.Model` object
+    """
+    return [f for f in search_dir.rglob("*") if f.suffix in {".mat", ".json", ".sbml", ".xml"}]
+
+
+async def ais_filepath_a_model(filepath: Path) -> cobra.Model | None:
+    """Asynchronously evaluate if a given filepath can be read as a `cobra.Model`.
+
+    :param filepath: The filepath to read
+    :return a `cobra.Model` object if the file can be read, otherwise None
+    """
+    suffix = filepath.suffix.lower()
+    if suffix == ".mat":
+        return await asyncio.to_thread(cobra.io.load_matlab_model, infile_path=filepath)
+    elif suffix in {".xml", ".sbml"}:
+        return await asyncio.to_thread(cobra.io.read_sbml_model, filename=filepath)
+    elif suffix in {".yaml", ".yml"}:
+        async with aiofiles.open(filepath) as i_stream:
+            content = await i_stream.read()
+        return cobra.io.from_yaml(content)
+    elif suffix == ".json":
+        async with aiofiles.open(filepath) as i_stream:
+            content = await i_stream.read()
+        data = orjson.loads(content)
+        return cobra.io.model_from_dict(data)
+    return None
+
+
+def get_cobra_model_if_valid(filepath: Path) -> cobra.Model | None:
+    """Evaluate if a given filepath can be read as a `cobra.Model`.
+
+    :param filepath: The filepath to read
+    :return a `cobra.Model` object if the file can be read, otherwise None
+    """
+    if filepath.suffix == ".json":
+        return cobra.io.load_json_model(filepath)
+    elif filepath.suffix == ".mat":
+        return cobra.io.load_matlab_model(filepath)
+    elif filepath.suffix in {".yaml", ".yml"}:
+        return cobra.io.load_yaml_model(filepath)
+    elif filepath.suffix in {".xml", ".sbml"}:
+        return cobra.io.read_sbml_model(filepath)
+    return None
+
+
+def get_model_flux(model: cobra.Model, objective: str = "biomass_maintenance", solver: str = "gurobi") -> pd.Series:
+    """Get the flux through a CBMM.
+
+    :param model: A `cobra.Model` object
+    :param objective: The objective function to optimize
+    :param solver: The solver to use
+    :return: A pandas Series of reaction fluxes indexed by reaction ID
+    """
+    model.objective = objective
+    model.solver = solver
+    solution = model.optimize()
+    return solution.fluxes
+
+
+def get_many_model_flux(
+    models: list[cobra.Model],
+    objective: str = "biomass_maintenance",
+    solver: str = "gurobi",
+    cores: int = 4,
+    process_pool: concurrent.futures.ProcessPoolExecutor | None = None,
+    colnames: list[str] | None = None,
+    na_value: NAType | int | float = NAType,
+) -> pd.DataFrame:
+    """Get the flux through many CBMMs.
+
+    :param models: A list of `cobra.Model` objects
+    :param objective: The objective function to optimize
+    :param solver: The solver to use
+    :param cores: The number of CPU cores to use
+    :param process_pool: An existing process pool to use
+    :param colnames: Column names to use for the resulting dataframe
+    :param na_value: Value to use for missing values in the dataframe
+    """
+    if colnames and len(colnames) != len(models):
+        raise ValueError("Length of colnames must match length of models")
+
+    pool = process_pool if process_pool else concurrent.futures.ProcessPoolExecutor(max_workers=cores)
+    shutdown = not process_pool  # if the user provided a pool, do not shut it down
+
+    func = partial(get_model_flux, objective=objective, solver=solver)
+    series: list[pd.Series] = list(pool.map(func, models))
+    for i, series_obj in enumerate(series):
+        series_obj.name = colnames[i] if colnames else f"model_{i}"
+    df: pd.DataFrame = pd.concat(list(series), axis="columns")
+
+    if shutdown:
+        pool.shutdown(wait=True)
+
+    if na_value != NAType:  # no need to replace values that are already pd.NA
+        df = df.fillna(na_value)
+
+    df = df.T
+    return df
+
+
+def group_reactions_by_pathway(models: cobra.Model | list[cobra.Model], flux_df: pd.DataFrame) -> pd.DataFrame:
+    pathways_by_reaction: dict[str, set[str]] = defaultdict(set)
+    models = [models] if isinstance(models, cobra.Model) else models
+
+    for model in models:
+        for reaction in model.reactions:
+            reaction: cobra.Reaction
+            pathways_by_reaction[reaction.subsystem].add(reaction.id)
+    pathways_by_reaction.pop("", None)  # remove the empty pathway; faster than checking every reaction's subsystem
+
+    # pathway_flux: pd.DataFrame = pd.DataFrame(index=flux_df.index, columns=list(pathways_by_reaction.keys()))
+    # for condition in flux_df.index:
+    #     for pathway, reactions in pathways_by_reaction.items():
+    #         pathway_flux.loc[condition, pathway] = flux_df.loc[condition, list(reactions)].sum()
+    pathway_fluxes: dict[str, pd.Series[npt.NDArray[np.floating]]] = {}
+    for pathway, reactions in pathways_by_reaction.items():
+        reactions_in_df = list(reactions.intersection(flux_df.columns))
+        if reactions_in_df:
+            pathway_fluxes[pathway] = flux_df[reactions_in_df].sum(axis=1)
+    return pd.DataFrame(pathway_fluxes)
+
+
+def build_condition_vs_pathway_heatmap(
+    data: pd.DataFrame | list[cobra.Model] | Path | list[Path],
+    save_filepath: Path | None = None,
+    objective: str = "biomass_maintenance",
+    solver: str = "gurobi",
+    process_pool: concurrent.futures.ProcessPoolExecutor | None = None,
+    cores: int = 4,
+    condition_names: list[str] | None = None,
+    na_value: NAType | int | float = NAType,
+    *,
+    search_path: bool = False,
+    copy_df_when_building_plot: bool = False,
+    exclude_zero_flux_pathways: bool = False,
+) -> plt.Figure:
+    """Create a heatmap of conditions vs flux through pathways.
+
+    If `data` is a pd.DataFrame:
+        - The index names wile used as conditions and placed on the Y-axis
+        - The column names will be used as pathways and placed on the X-axis. The columns should indicate pathways.
+
+    If `data` is a Path and `search_path` is True:
+        - Models will be recursively discovered under the given path
+        - Models will be simulated with the given objective and  solver
+        - A dataframe will be built from the resulting series based on the above rules
+
+    If `data` is a list of Paths:
+        - Models will be read and simulated for each path
+        - A pd.DataFrame will be built from the resulting series based on the above rules
+
+    If `data` is a list of cobra.Models:
+        - Models will be simulated with the given objective and solver
+        - A pd.DataFrame will be built from the resulting series based on the above rules
+
+    :param data: The data to use for the heatmap
+    :param search_path: Whether to search the given path for models
+    :param save_filepath: The filepath to save the heatmap to
+    :param objective: The objective function to optimize
+    :param solver: The solver to use
+    :param process_pool: An existing process pool to use
+    :param cores: The number of CPU cores to use
+    :param condition_names: Column names to use for the resulting dataframe if `data` is a Path or list of Paths
+    :param na_value: Value to use for missing values in the flux dataframe
+    :param copy_df_when_building_plot: Whether to copy the dataframe when building the plot. This can be useful if the dataframe is going to be reused later.
+    :param exclude_zero_flux_pathways: Whether to exclude pathways that have zero flux across all conditions
+
+
+    :return: A matplotlib Figure object containing the heatmap
+    """
+    if not isinstance(data, Path) and search_path:
+        raise ValueError("If search_path is True, data must be a Path")
+
+    flux_df: pd.DataFrame
+    if isinstance(data, pd.DataFrame):
+        return condition_vs_pathway(
+            data,
+            save_filepath=save_filepath,
+            copy_df=copy_df_when_building_plot,
+            exclude_zero_flux_pathways=exclude_zero_flux_pathways,
+        )
+    elif isinstance(data, list) and isinstance(data[0], cobra.Model):
+        models = data
+        flux_df = get_many_model_flux(
+            models=data,
+            objective=objective,
+            solver=solver,
+            cores=cores,
+            process_pool=process_pool,
+            colnames=condition_names,
+            na_value=na_value,
+        )
+    elif isinstance(data, Path):
+        if search_path:
+            possible_model_fps: list[Path] = find_possible_model_filepaths(data)
+            models = []
+            for fp in possible_model_fps:
+                if isinstance(model := get_cobra_model_if_valid(fp), cobra.Model):
+                    models.append(model)
+            flux_df = get_many_model_flux(
+                models,
+                objective=objective,
+                solver=solver,
+                cores=cores,
+                process_pool=process_pool,
+                colnames=condition_names,
+                na_value=na_value,
+            )
+        else:
+            models = get_cobra_model_if_valid(data)
+            flux_df = pd.DataFrame(get_model_flux(models, objective=objective, solver=solver))
+    elif isinstance(data, list) and isinstance(data[0], Path):
+        models = [get_cobra_model_if_valid(fp) for fp in data]
+        flux_df = get_many_model_flux(
+            models,
+            objective=objective,
+            solver=solver,
+            cores=cores,
+            process_pool=process_pool,
+            colnames=condition_names,
+            na_value=na_value,
+        )
+
+    flux_df = group_reactions_by_pathway(models=models, flux_df=flux_df)
+    return condition_vs_pathway(data=flux_df, save_filepath=save_filepath)
+
+
+async def _main():
+    models = [
+        Path("/home/joshl/projects/ImmunoMetabolism/data/model_build/A/A_pDCs/A_pDCs_model_imat.json"),
+        Path("/home/joshl/projects/ImmunoMetabolism/data/model_build/B/B_pDCs/B_pDCs_model_imat.json"),
+        Path("/home/joshl/projects/ImmunoMetabolism/data/model_build/C/C_pDCs/C_pDCs_model_imat.json"),
+        Path("/home/joshl/projects/ImmunoMetabolism/data/model_build/D/D_pDCs/D_pDCs_model_imat.json"),
+        Path("/home/joshl/projects/ImmunoMetabolism/data/model_build/E/E_pDCs/E_pDCs_model_imat.json"),
+    ]
+    save_path = Path(
+        f"/home/joshl/projects/ImmunoMetabolism/results/figures/{models[0].stem.removeprefix('A_').removesuffix('_model_imat')}_heatmap.png"
+    )
+
+    fig = build_condition_vs_pathway_heatmap(
+        data=models,
+        cores=5,
+        condition_names=["Age Group A", "Age Group B", "Age Group C", "Age Group D", "Age Group E"],
+        save_filepath=save_path,
+    )
+    fig.show()
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    asyncio.run(_main())

From d65a219c4ceed92201861325e06b41e16d06c807 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 5 Sep 2025 11:31:34 -0500
Subject: [PATCH 321/345] chore: fix typo

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 tests/unit/test_data_types.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/unit/test_data_types.py b/tests/unit/test_data_types.py
index 9688ddff..d5e1277a 100644
--- a/tests/unit/test_data_types.py
+++ b/tests/unit/test_data_types.py
@@ -2,7 +2,7 @@
 
 
 def test_source_types():
-    """Validate that source types always go in the order of 'trna', 'mrna', 'scrna', 'protemics'."""
+    """Validate that source types always go in the order of 'trna', 'mrna', 'scrna', 'proteomics'."""
     expected_order = ["trna", "mrna", "scrna", "proteomics"]
     for expected, source in zip(expected_order, SourceTypes):
         expected: str

From 3e671d9de4ee0916accd810674e0a0c1ecfea889 Mon Sep 17 00:00:00 2001
From: JoshLoecker <47901540+JoshLoecker@users.noreply.github.com>
Date: Fri, 5 Sep 2025 16:31:42 +0000
Subject: [PATCH 322/345] style: format code, Jupyter Notebook(s), and Python
 imports with `ruff`

---
 main/como/cluster_rnaseq.py                   | 12 +++--------
 main/como/data_types.py                       | 10 ++--------
 main/como/proteomics/Crux.py                  | 16 ++++-----------
 main/como/proteomics/FTPManager.py            |  4 +---
 main/como/proteomics/FileInformation.py       |  4 +---
 main/como/proteomics/proteomics_preprocess.py | 13 +++---------
 main/como/proteomics_preprocessing.py         | 20 +++++--------------
 7 files changed, 19 insertions(+), 60 deletions(-)

diff --git a/main/como/cluster_rnaseq.py b/main/como/cluster_rnaseq.py
index 2fb96cdc..1078b926 100644
--- a/main/como/cluster_rnaseq.py
+++ b/main/como/cluster_rnaseq.py
@@ -86,27 +86,21 @@ def __post_init__(self):  # noqa: C901, ignore too complex
                 level=LogLevel.ERROR,
             )
 
-        if (
-            isdigit(self.num_replicate_neighbors) and self.num_replicate_neighbors < 1
-        ) or self.num_replicate_neighbors != "default":
+        if (isdigit(self.num_replicate_neighbors) and self.num_replicate_neighbors < 1) or self.num_replicate_neighbors != "default":
             _log_and_raise_error(
                 "--n-neighbors-rep must be either 'default' or an integer > 1",
                 error=ValueError,
                 level=LogLevel.ERROR,
             )
 
-        if (
-            isdigit(self.num_batch_neighbors) and self.num_batch_neighbors < 1
-        ) or self.num_batch_neighbors != "default":
+        if (isdigit(self.num_batch_neighbors) and self.num_batch_neighbors < 1) or self.num_batch_neighbors != "default":
             _log_and_raise_error(
                 "--n-neighbors-batch must be either 'default' or an integer > 1",
                 error=ValueError,
                 level=LogLevel.ERROR,
             )
 
-        if (
-            isdigit(self.num_context_neighbors) and self.num_context_neighbors < 1
-        ) or self.num_context_neighbors != "default":
+        if (isdigit(self.num_context_neighbors) and self.num_context_neighbors < 1) or self.num_context_neighbors != "default":
             _log_and_raise_error(
                 "--n-neighbors-context must be either 'default' or an integer > 1",
                 error=ValueError,
diff --git a/main/como/data_types.py b/main/como/data_types.py
index f9c11ca5..420471a6 100644
--- a/main/como/data_types.py
+++ b/main/como/data_types.py
@@ -11,11 +11,7 @@
 from loguru import logger
 
 PATH_TYPE = str | Path
-LOG_FORMAT = (
-    "<green>{time:YYYY-MM-DD HH:mm:ss}</> | "
-    "<level>{level:<8}</> | "
-    "<cyan>{name}</>:<cyan>{line}</> - <level>{message}</>"
-)
+LOG_FORMAT = "<green>{time:YYYY-MM-DD HH:mm:ss}</> | <level>{level:<8}</> | <cyan>{name}</>:<cyan>{line}</> - <level>{message}</>"
 
 
 class AdjustmentMethod(Enum):
@@ -139,9 +135,7 @@ class CobraCompartments:
         "s": ["eyespot", "eyespot apparatus", "stigma"],
     }
 
-    _REVERSE_LOOKUP: ClassVar[dict[str, list[str]]] = {
-        value.lower(): key for key, values in SHORTHAND.items() for value in values
-    }
+    _REVERSE_LOOKUP: ClassVar[dict[str, list[str]]] = {value.lower(): key for key, values in SHORTHAND.items() for value in values}
 
     @classmethod
     def get_shorthand(cls, longhand: str) -> str | None:
diff --git a/main/como/proteomics/Crux.py b/main/como/proteomics/Crux.py
index b9d0107f..9351cf4e 100644
--- a/main/como/proteomics/Crux.py
+++ b/main/como/proteomics/Crux.py
@@ -128,9 +128,7 @@ def mzml_to_sqt(self) -> None:
             )
 
             # Replace all "comet.*" in output directory with the name of the file being processed
-            comet_files = [
-                str(file) for file in os.listdir(file_information.sqt_base_path) if str(file).startswith("comet.")
-            ]
+            comet_files = [str(file) for file in os.listdir(file_information.sqt_base_path) if str(file).startswith("comet.")]
             for file_name in comet_files:
                 # Determine the old file path
                 old_file_path: Path = Path(file_information.sqt_base_path, file_name)
@@ -242,9 +240,7 @@ def collect_uniprot_ids_and_ion_intensity(self) -> None:
 
             # Assign the file_information intensity dataframe to the gathered values
             self._file_information[i].intensity_df = pd.DataFrame(average_intensities_dict)
-            self._file_information[i].intensity_df = (
-                self._file_information[i].intensity_df.groupby("uniprot", as_index=False).mean()
-            )
+            self._file_information[i].intensity_df = self._file_information[i].intensity_df.groupby("uniprot", as_index=False).mean()
 
     async def _convert_uniprot_wrapper(self) -> None:
         """This function is a multiprocessing wrapper around the convert_ids function"""
@@ -252,9 +248,7 @@ async def _convert_uniprot_wrapper(self) -> None:
 
         # Create a progress bar of results
         # From: https://stackoverflow.com/a/61041328/
-        progress_bar = tqdm.tqdm(
-            desc="Starting UniProt to Gene Symbol conversion... ", total=len(self._file_information)
-        )
+        progress_bar = tqdm.tqdm(desc="Starting UniProt to Gene Symbol conversion... ", total=len(self._file_information))
         for i, result in enumerate(asyncio.as_completed(values)):
             await result  # Get result from asyncio.as_completed
             progress_bar.set_description(f"Working on {i + 1} of {len(self._file_information)}")
@@ -378,9 +372,7 @@ def split_abundance_values(self) -> None:
                 # Create a new dataframe to split the S# columns from
                 split_frame: pd.DataFrame = dataframe.copy()
                 # Get the current S{i} columns in
-                abundance_columns: list[str] = [
-                    column for column in split_frame.columns if re.match(rf"{cell_type}_S{i}R\d+", column)
-                ]
+                abundance_columns: list[str] = [column for column in split_frame.columns if re.match(rf"{cell_type}_S{i}R\d+", column)]
                 take_columns: list[str] = ["symbol"] + abundance_columns
                 average_intensity_name: str = f"{cell_type}_S{i}"
 
diff --git a/main/como/proteomics/FTPManager.py b/main/como/proteomics/FTPManager.py
index d2807cc8..b3b96052 100644
--- a/main/como/proteomics/FTPManager.py
+++ b/main/como/proteomics/FTPManager.py
@@ -21,9 +21,7 @@
 from como.data_types import LogLevel
 
 
-async def aioftp_client(
-    host: str, username: str = "anonymous", password: str = "guest", port: int = 21, max_attempts: int = 3
-) -> aioftp.Client:
+async def aioftp_client(host: str, username: str = "anonymous", password: str = "guest", port: int = 21, max_attempts: int = 3) -> aioftp.Client:
     """This class is responsible for creating a "client" connection"""
     connection_successful: bool = False
     attempt_num: int = 1
diff --git a/main/como/proteomics/FileInformation.py b/main/como/proteomics/FileInformation.py
index 0b37a4b6..59a4a2ee 100644
--- a/main/como/proteomics/FileInformation.py
+++ b/main/como/proteomics/FileInformation.py
@@ -68,9 +68,7 @@ def __init__(
             self.sqt_base_path: Path = sqt_path.parent
 
         if intensity_csv is None:
-            self.intensity_csv: Path = Path(
-                project.configs.data_dir, "data_matrices", cell_type, f"protein_abundance_matrix_{cell_type}.csv"
-            )
+            self.intensity_csv: Path = Path(project.configs.data_dir, "data_matrices", cell_type, f"protein_abundance_matrix_{cell_type}.csv")
         else:
             self.intensity_csv: Path = intensity_csv
 
diff --git a/main/como/proteomics/proteomics_preprocess.py b/main/como/proteomics/proteomics_preprocess.py
index 7a7df11d..86be1523 100644
--- a/main/como/proteomics/proteomics_preprocess.py
+++ b/main/como/proteomics/proteomics_preprocess.py
@@ -160,9 +160,7 @@ def _gather_data(self):
 
             # Iterate through the URLs available
             for url, study in zip(ftp_urls, studies):
-                ftp_data: FTPManager.Reader = FTPManager.Reader(
-                    root_link=url, file_extensions=self._preferred_extensions
-                )
+                ftp_data: FTPManager.Reader = FTPManager.Reader(root_link=url, file_extensions=self._preferred_extensions)
 
                 urls = list(ftp_data.files)
                 sizes = list(ftp_data.file_sizes)
@@ -170,9 +168,7 @@ def _gather_data(self):
 
                 # Iterate through all files and sizes found for url_##
                 for file, size in zip(urls, sizes):
-                    self.file_information.append(
-                        FileInformation(cell_type=cell_type, download_url=file, file_size=size, study=study)
-                    )
+                    self.file_information.append(FileInformation(cell_type=cell_type, download_url=file, file_size=size, study=study))
 
     def print_download_size(self):
         """Print the total size to download if we must download data."""
@@ -323,10 +319,7 @@ def parse_args() -> argparse.Namespace:
     else:
         args.core_count = int(args.core_count)
         if args.core_count > os.cpu_count():
-            logger.info(
-                f"{args.core_count} cores not available, system only has {os.cpu_count()} cores. "
-                f"Setting '--cores' to {os.cpu_count()}"
-            )
+            logger.info(f"{args.core_count} cores not available, system only has {os.cpu_count()} cores. Setting '--cores' to {os.cpu_count()}")
             args.core_count = os.cpu_count()
 
     return args
diff --git a/main/como/proteomics_preprocessing.py b/main/como/proteomics_preprocessing.py
index 1329406d..21f38a78 100644
--- a/main/como/proteomics_preprocessing.py
+++ b/main/como/proteomics_preprocessing.py
@@ -36,15 +36,9 @@ def z_score_calc(abundance: pd.DataFrame, min_thresh: int) -> ZResult:
 
     # np.zeros((1000, len(abundance.columns)), dtype=np.float64),
     z_result = ZResult(
-        zfpkm=pd.DataFrame(
-            data=np.nan * np.ones_like(values), index=abundance.index, columns=abundance.columns, dtype=np.float64
-        ),
-        x_range=pd.DataFrame(
-            data=np.zeros((1000, len(abundance.columns))), columns=abundance.columns, dtype=np.float64
-        ),
-        density=pd.DataFrame(
-            data=np.zeros((1000, len(abundance.columns))), columns=abundance.columns, dtype=np.float64
-        ),
+        zfpkm=pd.DataFrame(data=np.nan * np.ones_like(values), index=abundance.index, columns=abundance.columns, dtype=np.float64),
+        x_range=pd.DataFrame(data=np.zeros((1000, len(abundance.columns))), columns=abundance.columns, dtype=np.float64),
+        density=pd.DataFrame(data=np.zeros((1000, len(abundance.columns))), columns=abundance.columns, dtype=np.float64),
         mu=np.zeros(len(abundance.columns)),
         std_dev=np.zeros(len(abundance.columns)),
         max_fpkm_peak=np.zeros(len(abundance.columns)),
@@ -98,9 +92,7 @@ def plot_gaussian_fit(z_results: ZResult, facet_titles: bool = True, x_min: int
     std_dev = z_results.std_dev
     max_fpkm = z_results.max_fpkm_peak
 
-    fig = make_subplots(
-        rows=len(zfpkm.columns), cols=1, subplot_titles=zfpkm.columns if facet_titles else [None] * len(zfpkm.columns)
-    )
+    fig = make_subplots(rows=len(zfpkm.columns), cols=1, subplot_titles=zfpkm.columns if facet_titles else [None] * len(zfpkm.columns))
     for i, col in enumerate(zfpkm.columns):
         fitted = stats.norm.pdf(x_range[col], loc=mu[i], scale=std_dev[i])
         scale_fit = fitted * (max_fpkm[i] / fitted.max())
@@ -136,9 +128,7 @@ def protein_transform_main(
     output_z_score_matrix_filepath: Path,
 ) -> None:
     """Transform protein abundance data."""
-    abundance_df: pd.DataFrame = (
-        pd.read_csv(abundance_df) if isinstance(abundance_df, (str, Path)) else abundance_df.fillna(0)
-    )
+    abundance_df: pd.DataFrame = pd.read_csv(abundance_df) if isinstance(abundance_df, (str, Path)) else abundance_df.fillna(0)
     abundance_df = abundance_df[np.isfinite(abundance_df).all(axis=1)]  # Remove +/- infinity values
     z_transform: ZResult = z_score_calc(abundance_df, min_thresh=0)
 

From 1e0a5bb23d914f1a7acb56dbd948f000428a67fa Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 5 Sep 2025 11:34:35 -0500
Subject: [PATCH 323/345] fix: break up complex conditional

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index d4ba7335..6df3221f 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -532,7 +532,11 @@ def cpm_filter(
         top_samples = round(n_top * len(counts.columns))  # noqa: F841
         test_bools = pd.DataFrame({"entrez_gene_ids": entrez_ids})
         for i in range(len(counts_per_million.columns)):
-            cutoff = 10e6 / (np.median(np.sum(counts[:, i]))) if cut_off == "default" else (1e6 * cut_off) / np.median(np.sum(counts[:, i]))
+            median_sum = np.float64(np.median(np.sum(counts[:, i])))
+            if cut_off == "default":  # noqa: SIM108
+                cutoff = np.float64(10e6) / median_sum
+            else:
+                cutoff = np.float64(1e6 * cut_off) / median_sum
             test_bools = test_bools.merge(counts_per_million[counts_per_million.iloc[:, i] > cutoff])
 
     return metrics

From 6adaa7b1880e76a3d0f37c2afb632886f9b523f6 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 5 Sep 2025 11:36:10 -0500
Subject: [PATCH 324/345] fix: _validate_source_arguments requires arg
 expansion

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/merge_xomics.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index b613b92a..61153270 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -575,7 +575,7 @@ async def merge_xomics(  # noqa: C901
     }
     # fmt: on
     for source in source_data:
-        _validate_source_arguments(source, source_data[source])
+        _validate_source_arguments(source, *source_data[source])
 
     if all(
         file is None

From c26f437321bf1c6691529075f3de0ff27b748b87 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 5 Sep 2025 11:36:27 -0500
Subject: [PATCH 325/345] fix: typo

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/merge_xomics.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 61153270..fe46fc98 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -128,7 +128,7 @@ def _merge_logical_table(df: pd.DataFrame):
 
         df = pd.concat([df, pd.DataFrame(duplicate_rows)], axis=0, ignore_index=True)
         df.drop(df[df["entrez_gene_id"] == i].index, inplace=True)
-    logger.trace(f"Shape after merging duplciated rows: {df.shape}")
+    logger.trace(f"Shape after merging duplicated rows: {df.shape}")
 
     full_entrez_id_sets: set[str] = set()
     entrez_dups_list: list[list[str]] = []

From da2beaf62bf0c823bd5d197aa3f3f12ada9a3563 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 5 Sep 2025 11:36:49 -0500
Subject: [PATCH 326/345] fix: typo

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/data_types.py | 12 +++---------
 1 file changed, 3 insertions(+), 9 deletions(-)

diff --git a/main/como/data_types.py b/main/como/data_types.py
index f9c11ca5..43e00f4c 100644
--- a/main/como/data_types.py
+++ b/main/como/data_types.py
@@ -11,11 +11,7 @@
 from loguru import logger
 
 PATH_TYPE = str | Path
-LOG_FORMAT = (
-    "<green>{time:YYYY-MM-DD HH:mm:ss}</> | "
-    "<level>{level:<8}</> | "
-    "<cyan>{name}</>:<cyan>{line}</> - <level>{message}</>"
-)
+LOG_FORMAT = "<green>{time:YYYY-MM-DD HH:mm:ss}</> | <level>{level:<8}</> | <cyan>{name}</>:<cyan>{line}</> - <level>{message}</>"
 
 
 class AdjustmentMethod(Enum):
@@ -139,9 +135,7 @@ class CobraCompartments:
         "s": ["eyespot", "eyespot apparatus", "stigma"],
     }
 
-    _REVERSE_LOOKUP: ClassVar[dict[str, list[str]]] = {
-        value.lower(): key for key, values in SHORTHAND.items() for value in values
-    }
+    _REVERSE_LOOKUP: ClassVar[dict[str, list[str]]] = {value.lower(): key for key, values in SHORTHAND.items() for value in values}
 
     @classmethod
     def get_shorthand(cls, longhand: str) -> str | None:
@@ -215,7 +209,7 @@ def __setitem__(self, key, value):
 
     def _validate_attribute(self, key):
         if key not in {i.value for i in SourceTypes._member_map_.values()}:
-            # Unable to use como.utils._log_and_raise_error becuase it results in a circular import
+            # Unable to use como.utils._log_and_raise_error because it results in a circular import
             message = f"{key} is not a valid attribute of {SourceTypes.__name__}; got '{key}'"
             logger.warning(message)
             raise ValueError(message)

From 462ef3ff96f139c1a5948cdd59e5162282dce3d9 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Fri, 5 Sep 2025 13:17:33 -0500
Subject: [PATCH 327/345] refactor: simplify function signatures and improve
 docstrings

Updated several functions to use clearer argument names and added detailed docstrings following the Google style guide. Removed unnecessary async/await patterns where not needed, enhancing readability and maintainability.

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/COMO.ipynb                               |   7 +
 main/como/combine_distributions.py            |  39 ++++-
 main/como/create_context_specific_model.py    |  89 +++++++++-
 main/como/data_types.py                       |  25 ++-
 main/como/merge_xomics.py                     |  39 +++--
 main/como/migrations.py                       |   9 +-
 .../pipelines/build_condition_heatmaps.py     | 131 +++++++--------
 main/como/plot/heatmap.py                     |  14 +-
 main/como/project.py                          |  47 +++++-
 main/como/proteomics/proteomics_preprocess.py |  19 ++-
 main/como/proteomics_gen.py                   |  33 +++-
 main/como/proteomics_preprocessing.py         |  34 +++-
 main/como/rnaseq_gen.py                       | 154 +++++++++++++++---
 main/como/rnaseq_preprocess.py                |  29 +++-
 main/como/utils.py                            |  62 ++++---
 ruff.toml                                     |   4 +
 tests/unit/test_data_types.py                 |   2 +-
 tests/unit/test_rnaseq_preprocess.py          |   2 +-
 18 files changed, 566 insertions(+), 173 deletions(-)

diff --git a/main/COMO.ipynb b/main/COMO.ipynb
index 344dc8ae..df6df0b3 100644
--- a/main/COMO.ipynb
+++ b/main/COMO.ipynb
@@ -238,6 +238,13 @@
     "    \"\"\"Get the directory of the COMO.ipynb notebook.\n",
     "\n",
     "    This is used to set the proper input and output paths for the rest of the notebook\n",
+    "\n",
+    "\n",
+    "    Returns:\n",
+    "        The directory containing the `COMO.ipynb` notebook\n",
+    "\n",
+    "    Raises:\n",
+    "        FileNotFoundError: If the root directory could not be determined\n",
     "    \"\"\"\n",
     "    current_dir = Path().cwd()\n",
     "\n",
diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 5311fd9b..7549d1f6 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -23,7 +23,7 @@
 )
 
 
-async def _combine_z_distribution_for_batch(
+def _combine_z_distribution_for_batch(
     context_name: str,
     batch: _BatchEntry,
     matrix: pd.DataFrame,
@@ -33,6 +33,21 @@ async def _combine_z_distribution_for_batch(
     weighted_z_floor: int,
     weighted_z_ceiling: int,
 ) -> pd.DataFrame:
+    """Combine z-score distributions across samples for a single batch.
+
+    Args:
+        context_name: Name of the context (e.g., tissue or condition).
+        batch: Batch entry containing batch number and sample names.
+        matrix: DataFrame with 'ensembl_gene_id' and sample columns.
+        source: Source type (e.g., trna, mrna, scrna, proteomics).
+        output_combined_matrix_filepath: Path to save the combined z-score matrix.
+        output_figure_dirpath: Path to save the z-score distribution figure.
+        weighted_z_floor: Minimum z-score value after combining.
+        weighted_z_ceiling: Maximum z-score value after combining.
+
+    Returns:
+            A pandas dataframe of the weighted z-distributions
+    """
     output_combined_matrix_filepath.parent.mkdir(parents=True, exist_ok=True)
     output_figure_dirpath.mkdir(parents=True, exist_ok=True)
 
@@ -80,7 +95,7 @@ async def _combine_z_distribution_for_batch(
     return weighted_matrix
 
 
-async def _combine_z_distribution_for_source(
+def _combine_z_distribution_for_source(
     merged_source_data: pd.DataFrame,
     context_name: str,
     num_replicates: int,
@@ -88,7 +103,21 @@ async def _combine_z_distribution_for_source(
     output_figure_filepath: Path,
     weighted_z_floor: int = -6,
     weighted_z_ceiling: int = 6,
-):
+) -> pd.DataFrame:
+    """Combine z-score distributions across batches for a single source.
+
+    Args:
+        merged_source_data: DataFrame with 'ensembl_gene_id' and batch columns.
+        context_name: Name of the context (e.g., tissue or condition).
+        num_replicates: Number of replicates (samples) for weighting.
+        output_combined_matrix_filepath: Path to save the combined z-score matrix.
+        output_figure_filepath: Path to save the z-score distribution figure.
+        weighted_z_floor: Minimum z-score value after combining.
+        weighted_z_ceiling: Maximum z-score value after combining.
+
+    Returns:
+          A pandas dataframe of the weighted z-distributions
+    """
     if _num_columns(merged_source_data) <= 2:
         logger.warning("A single source exists, returning matrix as-is because no additional combining can be done")
         merged_source_data.columns = ["ensembl_gene_id", "combine_z"]
@@ -144,7 +173,7 @@ def _combine_z_distribution_for_context(
         return pd.DataFrame({"ensembl_gene_id": [], "combine_z": []})
 
     z_matrices = [
-        res.z_score_matrix.set_index("ensembl_gene_id").rename(columns={col: res.type.value for col in res.z_score_matrix.columns[1:]})
+        res.z_score_matrix.set_index("ensembl_gene_id").rename(columns=dict.fromkeys(res.z_score_matrix.columns[1:], res.type.value))
         for res in zscore_results
     ]
     z_matrix = pd.concat(z_matrices, axis=1, join="outer").reset_index()
@@ -239,7 +268,7 @@ async def _begin_combining_distributions(
         for df in batch_results:
             merged_batch_results = df if merged_batch_results.empty else merged_batch_results.merge(df, on="ensembl_gene_id", how="outer")
 
-        merged_source_results: pd.DataFrame = await _combine_z_distribution_for_source(
+        merged_source_results: pd.DataFrame = _combine_z_distribution_for_source(
             merged_source_data=merged_batch_results,
             context_name=context_name,
             num_replicates=sum(batch.num_samples for batch in batch_names[source.value]),
diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 6ac2327e..0a50890b 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -25,7 +25,15 @@
 
 
 def _correct_bracket(rule: str, name: str) -> str:
-    """Correct GPR rules to format readable by."""
+    """Correct GPR rules to format readable by.
+
+    Args:
+        rule: GPR rule string from a COBRA model
+        name: Gene name string from a COBRA model
+
+    Returns:
+        A corrected GPR rule string
+    """
     rule_match = re.search(r"or|and", rule)
     name_match = re.search(r"or|and", name)
     if rule_match is None or name_match is None:
@@ -54,7 +62,15 @@ def _correct_bracket(rule: str, name: str) -> str:
 
 
 def _gene_rule_logical(gpr_expression: str, level: int = 0) -> str:
-    """Create an expression from GPR rule which can be evaluated as true or false."""
+    """Create an expression from GPR rule which can be evaluated as true or false.
+
+    Args:
+        gpr_expression: GPR rule string from a COBRA model
+        level: Current recursion level (used for debugging)
+
+    Returns:
+        An evaluable string where "and" is replaced with "min" and "or" is replaced with "max"
+    """
     try:
         loc_r = gpr_expression.index(")")
     except ValueError:
@@ -95,9 +111,9 @@ def _set_boundaries(
     upper_bounds: list[float],
 ) -> cobra.Model:
     # get boundary reactions
-    exchange_rxns = [rxn.id for rxn in model.reactions if re.search("EX_", rxn.id)]
-    sink_rxns = [rxn.id for rxn in model.reactions if re.search("sink_", rxn.id)]
-    demand_rxns = [rxn.id for rxn in model.reactions if re.search("DM_", rxn.id)]
+    exchange_rxns = [rxn.id for rxn in model.reactions if "EX_" in rxn.id]
+    sink_rxns = [rxn.id for rxn in model.reactions if "sink_" in rxn.id]
+    demand_rxns = [rxn.id for rxn in model.reactions if "DM_" in rxn.id]
 
     # Allows all boundary reactions to be used if none are given
     allow_all_boundary_rxns = not boundary_reactions
@@ -181,7 +197,7 @@ def _build_with_fastcore(cobra_model, s_matrix, lower_bounds, upper_bounds, exp_
     # e1003424.'
     logger.warning("Fastcore requires a flux consistant model is used as refererence, to achieve this fastcc is required which is NOT reproducible.")
     logger.debug("Creating feasible model")
-    incon_rxns, cobra_model = _feasibility_test(cobra_model, "other")
+    _, cobra_model = _feasibility_test(cobra_model, "other")
     properties = FastcoreProperties(core=exp_idx_list, solver=solver)
     algorithm = FASTcore(s_matrix, lower_bounds, upper_bounds, properties)
     context_rxns = algorithm.fastcore()
@@ -263,6 +279,19 @@ async def _map_expression_to_reaction(
     """Map gene ids to a reaction based on GPR (gene to protein to reaction) association rules.
 
     These rules should be defined in the general genome-scale metabolic model
+
+    Args:
+        reference_model: A COBRA model object representing the general genome-scale metabolic model.
+        gene_expression_file: Path to a gene expression file (.csv, .tsv, .xlsx, or .xls)
+        recon_algorithm: Algorithm to use for reconstruction (GIMME, FASTCORE, iMAT, or tINIT)
+        low_thresh: Low expression threshold for algorithms that require it (iMAT, tINIT)
+        high_thresh: High expression threshold for algorithms that require it (iMAT, tINIT)
+
+    Returns:
+        An ordered dictionary mapping reaction IDs to their corresponding expression values.
+
+    Raises:
+        ValueError: If neither 'entrez_gene_id' nor 'ensembl_gene_id' columns are found in the gene expression file.
     """
     expression_data = await _read_file(gene_expression_file)
     identifier_column = next((col for col in ("entrez_gene_id", "ensembl_gene_id") if col in expression_data.columns), "")
@@ -282,8 +311,8 @@ async def _map_expression_to_reaction(
     # Define a default expression value if a gene ID is not found
     default_expression = (
         np.mean([low_thresh, high_thresh]) if recon_algorithm in {Algorithm.IMAT, Algorithm.TINIT}
-        else -1 if recon_algorithm in {Algorithm.GIMME}
-        else 0 if recon_algorithm in {Algorithm.FASTCORE}
+        else -1 if recon_algorithm == Algorithm.GIMME
+        else 0 if recon_algorithm == Algorithm.FASTCORE
         else 1
     )
     # fmt: on
@@ -339,6 +368,24 @@ async def _build_model(  # noqa: C901
     Core reactions that do not necessarily meet GPR association requirements can be forced if in the force reaction
     file. Metabolite exchange (media), sinks, and demands are determined from exchanges file. Reactions can also be
     force excluded even if they meet GPR association requirements using the force exclude file.
+
+    Args:
+        general_model_file: Path to a COBRA model file (.xml, .mat, or .json)
+        gene_expression_file: Path to a gene expression file (.csv, .tsv, .xlsx, or .xls)
+        recon_algorithm: Algorithm to use for reconstruction (GIMME, FASTCORE, iMAT, or tINIT)
+        objective: Objective reaction ID in the general model
+        boundary_reactions: List of boundary reactions to set in the model
+        exclude_reactions: List of reactions to exclude from the model
+        force_reactions: List of reactions to force include in the model
+        lower_bounds: List of lower bounds corresponding to boundary reactions
+        upper_bounds: List of upper bounds corresponding to boundary reactions
+        solver: Solver to use (e.g., 'glpk', 'cplex', 'gurobi')
+        low_thresh: Low expression threshold for algorithms that require it (iMAT, tINIT)
+        high_thresh: High expression threshold for algorithms that require it (iMAT, tINIT)
+        output_flux_result_filepath: Path to save flux results (for iMAT only)
+
+    Returns:
+        A _BuildResults object containing the context-specific model, list of expression indices used, and a DataFrame of infeasible reactions.
     """
     reference_model: cobra.Model
     match general_model_file.suffix:
@@ -583,7 +630,31 @@ async def create_context_specific_model(  # noqa: C901
     log_level: LogLevel = LogLevel.INFO,
     log_location: str | TextIOWrapper = sys.stderr,
 ):
-    """Create a context-specific model using the provided data."""
+    """Create a context-specific model using the provided data.
+
+    Args:
+        context_name: Name of the context-specific model.
+        reference_model: Path to the general genome-scale metabolic model file (.xml, .mat, or .json).
+        active_genes_filepath: Path to the gene expression data file (csv, tsv, or Excel).
+        output_infeasible_reactions_filepath: Path to save infeasible reactions (csv).
+        output_flux_result_filepath: Path to save flux results (csv).
+        output_model_filepaths: Path or list of paths to save the context-specific model (.xml, .mat, or .json).
+        output_filetypes: List of file types to save the model as ('xml', 'mat', 'json').
+        output_fastcore_expression_index_filepath: Path to save Fastcore expression indices (txt). Required if using Fastcore.
+        objective: Objective function reaction ID.
+        boundary_rxns_filepath: Optional path to boundary reactions file (csv, tsv, or Excel).
+        exclude_rxns_filepath: Optional path to reactions to exclude file (csv, tsv, or Excel).
+        force_rxns_filepath: Optional path to reactions to force include file (csv, tsv, or Excel).
+        algorithm: Algorithm to use for reconstruction. One of Algorithm.GIMME, Algorithm.FASTCORE, Algorithm.IMAT, Algorithm.TINIT.
+        low_threshold: Low expression threshold for algorithms that require it.
+        high_threshold: High expression threshold for algorithms that require it.
+        solver: Solver to use. One of Solver.GLPK, Solver.CPLEX, Solver.GUROBI
+        log_level: Logging level. One of LogLevel.DEBUG, LogLevel.INFO, LogLevel.WARNING, LogLevel.ERROR, LogLevel.CRITICAL
+        log_location: Location for log output. Can be a file path or sys.stderr/sys.stdout.
+
+    Raises:
+        ImportError: If Gurobi solver is selected but gurobipy is not installed.
+    """
     _set_up_logging(level=log_level, location=log_location)
     output_model_filepaths = [output_model_filepaths] if isinstance(output_model_filepaths, Path) else output_model_filepaths
     for path in output_model_filepaths:
diff --git a/main/como/data_types.py b/main/como/data_types.py
index 43e00f4c..177edf6f 100644
--- a/main/como/data_types.py
+++ b/main/como/data_types.py
@@ -139,12 +139,26 @@ class CobraCompartments:
 
     @classmethod
     def get_shorthand(cls, longhand: str) -> str | None:
-        """Get the short-hand compartment name from the long-hand name."""
+        """Get the short-hand compartment name from the long-hand name.
+
+        Args:
+            longhand: The long-hand compartment name (e.g., 'cytoplasm', 'extracellular').
+
+        Returns:
+            The short-hand compartment name if found, None otherwise.
+        """
         return cls._REVERSE_LOOKUP.get(longhand.lower(), None)
 
     @classmethod
     def get_longhand(cls, shorthand: str) -> str | None:
-        """Get the long-hand compartment name from the short-hand name."""
+        """Get the long-hand compartment name from the short-hand name.
+
+        Args:
+            shorthand: The short-hand compartment name (e.g., 'c', 'e', 'm').
+
+        Returns:
+            The long-hand compartment name if found, None otherwise.
+        """
         longhand = cls.SHORTHAND.get(shorthand.lower(), None)
         return longhand[0] if longhand else None
 
@@ -192,8 +206,11 @@ class _BaseDataType:
     def __getitem__(self, value: str):
         """Access matrices using square bracket notation (e.g., `input_matrices['total_rna']`).
 
-        :param value: The name of the matrix to get ('trna', 'mrna', 'scrna', 'proteomics')
-        :returns: The DataFrame if it exists, None otherwise.
+        Args:
+            value: The name of the matrix to get ('trna', 'mrna', 'scrna', 'proteomics')
+
+        Returns:
+            The DataFrame if it exists, None otherwise.
         """
         self._validate_attribute(value)
         return getattr(self, value)
diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index fe46fc98..a8b81652 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -50,8 +50,17 @@ class _HighExpressionHeaderNames:
 
 
 # TODO: If function is no longer needed, remove?
-async def _load_rnaseq_tests(filename, context_name, prep_method: RNAType) -> tuple[str, pd.DataFrame]:
-    """Load rnaseq results."""
+def _load_rnaseq_tests(filename, context_name, prep_method: RNAType) -> tuple[str, pd.DataFrame]:
+    """Load rnaseq results.
+
+    Args:
+        filename: Name of the file to load
+        context_name: Name of the context (e.g., tissue or cell type)
+        prep_method: The RNA-seq library preparation method (e.g., mRNA, total RNA, single-cell RNA)
+
+    Returns:
+        A tuple containing the context name and the loaded DataFrame±
+    """
     logger.debug(f"Loading data for context '{context_name}' using preparation method '{prep_method.value}'")
     config = Config()
 
@@ -103,8 +112,11 @@ def load_dummy_dict():
 def _merge_logical_table(df: pd.DataFrame):
     """Merge rows of Logical Table belonging to the same entrez_gene_id.
 
-    :param df: pandas dataframe of logical table
-    :return: pandas dataframe of merged table
+    Args:
+        df: Pandas dataframe containing the logical table
+
+    Returns:
+        pandas dataframe of merged table
     """
     # step 1: get all plural ENTREZ_GENE_IDs in the input table, extract unique IDs
     df.dropna(subset=["entrez_gene_id"], inplace=True)
@@ -135,6 +147,7 @@ def _merge_logical_table(df: pd.DataFrame):
     multi_entrez_index = list(range(len(multiple_entrez_ids)))
 
     logger.trace("Starting to merge multiple entrez IDs")
+    temp_multi_entrez_index = multi_entrez_index.copy()
     for i in range(len(multiple_entrez_ids)):
         if i not in multi_entrez_index:
             continue
@@ -142,23 +155,25 @@ def _merge_logical_table(df: pd.DataFrame):
         logger.trace(f"Iterating through multi-entrez ids, index {i}")
 
         set1 = set(multiple_entrez_ids[i].split("//"))
-        multi_entrez_index.remove(i)
+        temp_multi_entrez_index.remove(i)
 
         for j in multi_entrez_index:
             set2 = set(multiple_entrez_ids[j].split("//"))
             intersect = set1.intersection(set2)
             if bool(intersect):
                 set1 = set1.union(set2)
-                multi_entrez_index.remove(j)
+                temp_multi_entrez_index.remove(j)
 
         sortlist = list(set1)
         sortlist.sort(key=int)
         new_entrez_id = " /// ".join(sortlist)
         full_entrez_id_sets.add(new_entrez_id)
 
+    multi_entrez_index = temp_multi_entrez_index.copy()
+
     logger.debug(f"Finished merging multiple entrez IDs, found {len(full_entrez_id_sets)} sets")
     entrez_dups_list.extend(i.split(" /// ") for i in full_entrez_id_sets)
-    entrez_dups_dict = dict(zip(full_entrez_id_sets, entrez_dups_list))
+    entrez_dups_dict = dict(zip(full_entrez_id_sets, entrez_dups_list, strict=True))
 
     logger.trace("Replacing IDs in dataframe")
     for merged_entrez_id, entrez_dups_list in entrez_dups_dict.items():
@@ -193,8 +208,12 @@ async def _get_transcriptmoic_details(merged_df: pd.DataFrame, taxon_id: int) ->
     The resulting dataframe will have its columns created in the order listed above
     It will return a pandas dataframe with this information
 
-    :param merged_df: A dataframe containing all active transcriptomic and proteomic genes
-    :return: A dataframe with the above-listed columns
+    Args:
+        merged_df: A dataframe containing all active transcriptomic and proteomic genes
+        taxon_id: The NCBI taxonomy ID of the organism
+
+    Returns:
+        A dataframe with the above-listed columns
     """
     # If _ExpressedHeaderNames.PROTEOMICS.value is in the dataframe, lower the required expression by 1
     # We are only trying to get details for transcriptomic data
@@ -486,7 +505,7 @@ def _build_batches(
     proteomic_metadata: pd.DataFrame | None,
 ) -> _BatchNames:
     batch_names = _BatchNames(**{source.name.lower(): [] for source in SourceTypes})
-    for source, metadata in zip(SourceTypes, [trna_metadata, mrna_metadata, scrna_metadata, proteomic_metadata]):
+    for source, metadata in zip(SourceTypes, [trna_metadata, mrna_metadata, scrna_metadata, proteomic_metadata], strict=True):
         source: SourceTypes
         metadata: pd.DataFrame
         if metadata is None:
diff --git a/main/como/migrations.py b/main/como/migrations.py
index 61ac6abf..c1d291b1 100644
--- a/main/como/migrations.py
+++ b/main/como/migrations.py
@@ -2,5 +2,12 @@
 
 
 def gene_info_migrations(df: pd.DataFrame) -> pd.DataFrame:
-    """Migrate gene info DataFrame to the latest version."""
+    """Migrate gene info DataFrame to the latest version.
+
+    Args:
+        df: The input DataFrame containing gene information.
+
+    Returns:
+        The migrated DataFrame with updated column names.
+    """
     return df.rename(columns={"hgnc_symbol": "gene_symbol"}) if "hgnc_symbol" in df.columns else df
diff --git a/main/como/pipelines/build_condition_heatmaps.py b/main/como/pipelines/build_condition_heatmaps.py
index a0161a45..16b970b8 100644
--- a/main/como/pipelines/build_condition_heatmaps.py
+++ b/main/como/pipelines/build_condition_heatmaps.py
@@ -10,65 +10,36 @@
 from functools import partial
 from pathlib import Path
 
-import aiofiles.os
-import aiofiles.ospath
 import cobra
 import matplotlib.pyplot as plt
 import numpy as np
 import numpy.typing as npt
-import orjson
 import pandas as pd
 from pandas._libs.missing import NAType
 
 from como.plot.heatmap import condition_vs_pathway
 
 
-async def afind_possible_model_filepaths(search_dir: Path) -> list[Path]:
-    """Asynchronously find potential files that could be constraint-based metabolic models.
-
-    :param search_dir: The directory to search for models.
-    :return: Potential filepaths that could be loaded as a `cobra.Model` object
-    """
-    return [f for f in list(await asyncio.to_thread(search_dir.rglob, pattern="*")) if f.suffix in {".mat", ".json", ".sbml", ".xml"}]
-
-
 def find_possible_model_filepaths(search_dir: Path) -> list[Path]:
     """Find potential files that could be constraint-based metabolic models.
 
-    :param search_dir: The directory to search for models.
-    :return: Potential filepaths that could be loaded as a `cobra.Model` object
-    """
-    return [f for f in search_dir.rglob("*") if f.suffix in {".mat", ".json", ".sbml", ".xml"}]
-
-
-async def ais_filepath_a_model(filepath: Path) -> cobra.Model | None:
-    """Asynchronously evaluate if a given filepath can be read as a `cobra.Model`.
+    Args:
+        search_dir: The directory to search for models.
 
-    :param filepath: The filepath to read
-    :return a `cobra.Model` object if the file can be read, otherwise None
+    Returns:
+        Potential filepaths that could be loaded as a `cobra.Model` object
     """
-    suffix = filepath.suffix.lower()
-    if suffix == ".mat":
-        return await asyncio.to_thread(cobra.io.load_matlab_model, infile_path=filepath)
-    elif suffix in {".xml", ".sbml"}:
-        return await asyncio.to_thread(cobra.io.read_sbml_model, filename=filepath)
-    elif suffix in {".yaml", ".yml"}:
-        async with aiofiles.open(filepath) as i_stream:
-            content = await i_stream.read()
-        return cobra.io.from_yaml(content)
-    elif suffix == ".json":
-        async with aiofiles.open(filepath) as i_stream:
-            content = await i_stream.read()
-        data = orjson.loads(content)
-        return cobra.io.model_from_dict(data)
-    return None
+    return [f for f in search_dir.rglob("*") if f.suffix in {".mat", ".json", ".sbml", ".xml"}]
 
 
 def get_cobra_model_if_valid(filepath: Path) -> cobra.Model | None:
     """Evaluate if a given filepath can be read as a `cobra.Model`.
 
-    :param filepath: The filepath to read
-    :return a `cobra.Model` object if the file can be read, otherwise None
+    Args:
+        filepath: The filepath to read
+
+    Returns:
+        a `cobra.Model` object if the file can be read, otherwise None
     """
     if filepath.suffix == ".json":
         return cobra.io.load_json_model(filepath)
@@ -84,10 +55,13 @@ def get_cobra_model_if_valid(filepath: Path) -> cobra.Model | None:
 def get_model_flux(model: cobra.Model, objective: str = "biomass_maintenance", solver: str = "gurobi") -> pd.Series:
     """Get the flux through a CBMM.
 
-    :param model: A `cobra.Model` object
-    :param objective: The objective function to optimize
-    :param solver: The solver to use
-    :return: A pandas Series of reaction fluxes indexed by reaction ID
+    Args:
+        model: A `cobra.Model` object
+        objective: The objective function to optimize
+        solver: The solver to use
+
+    Returns:
+        A pandas Series of reaction fluxes indexed by reaction ID
     """
     model.objective = objective
     model.solver = solver
@@ -106,18 +80,25 @@ def get_many_model_flux(
 ) -> pd.DataFrame:
     """Get the flux through many CBMMs.
 
-    :param models: A list of `cobra.Model` objects
-    :param objective: The objective function to optimize
-    :param solver: The solver to use
-    :param cores: The number of CPU cores to use
-    :param process_pool: An existing process pool to use
-    :param colnames: Column names to use for the resulting dataframe
-    :param na_value: Value to use for missing values in the dataframe
+    Args:
+        models: A list of `cobra.Model` objects
+        objective: The objective function to optimize
+        solver: The solver to use
+        cores: The number of CPU cores to use
+        process_pool: An existing process pool to use
+        colnames: Column names to use for the resulting dataframe
+        na_value: Value to use for missing values in the dataframe
+
+    Returns:
+        A pandas DataFrame of reaction fluxes indexed by condition (row) and reaction ID (column)
+
+    Raises:
+        ValueError: If `colnames` is provided and its length does not match the number of models
     """
     if colnames and len(colnames) != len(models):
         raise ValueError("Length of colnames must match length of models")
 
-    pool = process_pool if process_pool else concurrent.futures.ProcessPoolExecutor(max_workers=cores)
+    pool = process_pool or concurrent.futures.ProcessPoolExecutor(max_workers=cores)
     shutdown = not process_pool  # if the user provided a pool, do not shut it down
 
     func = partial(get_model_flux, objective=objective, solver=solver)
@@ -137,6 +118,15 @@ def get_many_model_flux(
 
 
 def group_reactions_by_pathway(models: cobra.Model | list[cobra.Model], flux_df: pd.DataFrame) -> pd.DataFrame:
+    """Group reactions by their subsystem/pathway and sum the fluxes.
+
+    Args:
+        models: A cobra.Model or list of cobra.Models
+        flux_df: A dataframe of reaction fluxes, indexed by condition and with reaction IDs as columns
+
+    Returns:
+        A dataframe of pathway fluxes, indexed by condition and with pathways as columns
+    """
     pathways_by_reaction: dict[str, set[str]] = defaultdict(set)
     models = [models] if isinstance(models, cobra.Model) else models
 
@@ -191,20 +181,25 @@ def build_condition_vs_pathway_heatmap(
         - Models will be simulated with the given objective and solver
         - A pd.DataFrame will be built from the resulting series based on the above rules
 
-    :param data: The data to use for the heatmap
-    :param search_path: Whether to search the given path for models
-    :param save_filepath: The filepath to save the heatmap to
-    :param objective: The objective function to optimize
-    :param solver: The solver to use
-    :param process_pool: An existing process pool to use
-    :param cores: The number of CPU cores to use
-    :param condition_names: Column names to use for the resulting dataframe if `data` is a Path or list of Paths
-    :param na_value: Value to use for missing values in the flux dataframe
-    :param copy_df_when_building_plot: Whether to copy the dataframe when building the plot. This can be useful if the dataframe is going to be reused later.
-    :param exclude_zero_flux_pathways: Whether to exclude pathways that have zero flux across all conditions
-
-
-    :return: A matplotlib Figure object containing the heatmap
+    Args:
+        data: The data to use for the heatmap
+        search_path: Whether to search the given path for models
+        save_filepath: The filepath to save the heatmap to
+        objective: The objective function to optimize
+        solver: The solver to use
+        process_pool: An existing process pool to use
+        cores: The number of CPU cores to use
+        condition_names: Column names to use for the resulting dataframe if `data` is a Path or list of Paths
+        na_value: Value to use for missing values in the flux dataframe
+        copy_df_when_building_plot: Whether to copy the dataframe when building the plot.
+            This can be useful if the dataframe is going to be reused later.
+        exclude_zero_flux_pathways: Whether to exclude pathways that have zero flux across all conditions
+
+    Returns:
+        A matplotlib Figure object containing the heatmap
+
+    Raises:
+        ValueError: If `search_path` is True and `data` is not a Path
     """
     if not isinstance(data, Path) and search_path:
         raise ValueError("If search_path is True, data must be a Path")
@@ -263,7 +258,7 @@ def build_condition_vs_pathway_heatmap(
     return condition_vs_pathway(data=flux_df, save_filepath=save_filepath)
 
 
-async def _main():
+def _main():
     models = [
         Path("/home/joshl/projects/ImmunoMetabolism/data/model_build/A/A_pDCs/A_pDCs_model_imat.json"),
         Path("/home/joshl/projects/ImmunoMetabolism/data/model_build/B/B_pDCs/B_pDCs_model_imat.json"),
@@ -285,6 +280,4 @@ async def _main():
 
 
 if __name__ == "__main__":
-    import asyncio
-
-    asyncio.run(_main())
+    _main()
diff --git a/main/como/plot/heatmap.py b/main/como/plot/heatmap.py
index cfa21c64..0d6e4e55 100644
--- a/main/como/plot/heatmap.py
+++ b/main/como/plot/heatmap.py
@@ -20,12 +20,14 @@ def condition_vs_pathway(
 ) -> plt.Figure:
     """Build a heatmap of fluxes through pathways across conditions.
 
-    :param data: Index values are conditions, column names are pathways
-    :param save_filepath: If provided, the resulting figure will be saved to this location
-    :param copy_df: Should the incoming dataframe be copied to prevent modifications to data?
-    :param exclude_zero_flux_pathways: Should pathways that have 0 flux across all rows be excluded?
-
-    :return: The resulting `matpotlib.pyplt.Figure` object
+    Args:
+        data: Index values are conditions, column names are pathways
+        save_filepath: If provided, the resulting figure will be saved to this location
+        copy_df: Should the incoming dataframe be copied to prevent modifications to data?
+        exclude_zero_flux_pathways: Should pathways that have 0 flux across all rows be excluded?
+
+    Returns:
+        The resulting `matpotlib.pyplt.Figure` object
     """
     plot_df: pd.DataFrame = data.copy() if copy_df else data
     plot_df = plot_df.astype(np.float32)
diff --git a/main/como/project.py b/main/como/project.py
index b6a21bf9..19779940 100644
--- a/main/como/project.py
+++ b/main/como/project.py
@@ -9,8 +9,16 @@
 class SingletonMeta(type):
     _instances: ClassVar[dict] = {}
 
-    def __call__(cls, *args, **kwargs):
-        """Validate that changes to the `__init__` argument do not affect the returned instance."""
+    def __call__(cls, *args, **kwargs) -> SingletonMeta:
+        """Validate that changes to the `__init__` argument do not affect the returned instance.
+
+        Args:
+            args: Positional arguments for the class constructor
+            kwargs: Keyword arguments for the class constructor
+
+        Returns:
+            The singleton instance of the class
+        """
         if cls not in cls._instances:
             instance = super().__call__(*args, **kwargs)
             cls._instances[cls] = instance
@@ -56,7 +64,11 @@ def __init__(
         self.figures_dir.mkdir(parents=True, exist_ok=True)
 
     def update(self, **kwargs):
-        """Update a key in the config object."""
+        """Update a key in the config object.
+
+        Args:
+            kwargs: keyword argumentsto set for the singleton
+        """
         for key, value in kwargs.items():
             if hasattr(self, key):
                 setattr(self, key, Path(value) if value else getattr(self, key))
@@ -64,18 +76,41 @@ def update(self, **kwargs):
                 logger.warning(f"{key} is not a valid attribute of Config")
 
     def get_context_path(self, context_name: str, create: bool = True) -> Path:
-        """Get path for a specific context, optionally creating it."""
+        """Get path for a specific context, optionally creating it.
+
+        Args:
+            context_name: Name of the context (subdirectory).
+            create: Whether to create the directory if it doesn't exist.
+
+        Returns:
+            Full path to the context directory.
+        """
         path = self.result_dir / context_name
         if create:
             path.mkdir(parents=True, exist_ok=True)
         return path
 
     def get_r_path(self, path: Path) -> str:
-        """Convert a Path object to an R-compatible path string."""
+        """Convert a Path object to an R-compatible path string.
+
+        Args:
+            path: Path object to convert.
+
+        Returns:
+            R-compatible path string.
+        """
         return path.as_posix()
 
     def get_matrix_path(self, context_name: str, filename: str) -> Path:
-        """Get path for a matrix file in a specific context."""
+        """Get path for a matrix file in a specific context.
+
+        Arg:
+            context_name: Name of the context (subdirectory).
+            filename: Name of the matrix file.
+
+        Returns:
+            Full path to the matrix file.
+        """
         path = self.matrix_dir / context_name
         path.mkdir(parents=True, exist_ok=True)
         return path / filename
diff --git a/main/como/proteomics/proteomics_preprocess.py b/main/como/proteomics/proteomics_preprocess.py
index 86be1523..b2223da7 100644
--- a/main/como/proteomics/proteomics_preprocess.py
+++ b/main/como/proteomics/proteomics_preprocess.py
@@ -159,7 +159,7 @@ def _gather_data(self):
             url_count = 0
 
             # Iterate through the URLs available
-            for url, study in zip(ftp_urls, studies):
+            for url, study in zip(ftp_urls, studies, strict=True):
                 ftp_data: FTPManager.Reader = FTPManager.Reader(root_link=url, file_extensions=self._preferred_extensions)
 
                 urls = list(ftp_data.files)
@@ -167,7 +167,7 @@ def _gather_data(self):
                 url_count += len(urls)
 
                 # Iterate through all files and sizes found for url_##
-                for file, size in zip(urls, sizes):
+                for file, size in zip(urls, sizes, strict=True):
                     self.file_information.append(FileInformation(cell_type=cell_type, download_url=file, file_size=size, study=study))
 
     def print_download_size(self):
@@ -205,12 +205,23 @@ def _set_replicate_numbers(self):
                 current_info.set_replicate(replicate_value)
 
     def _collect_cell_type_information(self, cell_type: str) -> list[FileInformation]:
-        """Collect all FileInformation objects of a given cell type."""
+        """Collect all FileInformation objects of a given cell type.
+
+        Arg:
+            cell_type: The cell type to collect information for.
+
+        Returns:
+            A list of FileInformation objects matching the given cell type.
+        """
         return [information for information in self.file_information if information.cell_type == cell_type]
 
 
 def parse_args() -> argparse.Namespace:
-    """Parse arguments from the command line."""
+    """Parse arguments from the command line.
+
+    Returns:
+        An argparse.Namespace object containing the parsed arguments.
+    """
     parser = argparse.ArgumentParser(
         prog="proteomics_preprocess.py",
         description="Download and analyze proteomics data from proteomeXchange\n"
diff --git a/main/como/proteomics_gen.py b/main/como/proteomics_gen.py
index 102490ba..012486a4 100644
--- a/main/como/proteomics_gen.py
+++ b/main/como/proteomics_gen.py
@@ -17,7 +17,14 @@
 
 # Load Proteomics
 def process_proteomics_data(path: Path) -> pd.DataFrame:
-    """Load proteomics data from a given context and filename."""
+    """Load proteomics data from a given context and filename.
+
+    Args:
+        path: Path to the proteomics data file (CSV format).
+
+    Returns:
+        pd.DataFrame: Processed proteomics data with 'gene_symbol' column exploded.
+    """
     # Preprocess data, drop na, duplicate ';' in symbol,
     matrix: pd.DataFrame = pd.read_csv(path)
     if "gene_symbol" not in matrix.columns:
@@ -35,7 +42,15 @@ def process_proteomics_data(path: Path) -> pd.DataFrame:
 
 # read map to convert to entrez
 async def load_gene_symbol_map(gene_symbols: list[str], entrez_map: Path | None = None):
-    """Add descirption...."""
+    """Load a mapping from gene symbols to Entrez IDs.
+
+    Args:
+        gene_symbols (list[str]): List of gene symbols to map.
+        entrez_map (Path | None): Optional path to a CSV file containing precomputed mappings.
+
+    Returns:
+        pd.DataFrame: DataFrame with gene symbols as index and corresponding Entrez IDs.
+    """
     if entrez_map and entrez_map.exists():
         df = pd.read_csv(entrez_map, index_col="gene_symbol")
     else:
@@ -122,8 +137,16 @@ def to_bool_context(context_name, group_ratio, hi_group_ratio, group_names):
 
 
 # read data from csv files
-def load_proteomics_tests(filename, context_name):
-    """Load statistical test results."""
+def load_proteomics_tests(filename, context_name) -> tuple[str, pd.DataFrame]:
+    """Load statistical test results.
+
+    Arg:
+        filename (str): The name of the file to load.
+        context_name (str): The context name for the data.
+
+    Returns:
+        tuple: A tuple containing the context name and the loaded data as a pandas DataFrame.
+    """
     config = Config()
 
     def load_empty_dict():
@@ -187,7 +210,7 @@ async def proteomics_gen(
             error=FileNotFoundError,
             level=LogLevel.ERROR,
         )
-    if matrix_filepath.suffix not in {".csv"}:
+    if matrix_filepath.suffix != ".csv":
         _log_and_raise_error(
             f"Matrix file must be a csv file at {matrix_filepath}",
             error=FileNotFoundError,
diff --git a/main/como/proteomics_preprocessing.py b/main/como/proteomics_preprocessing.py
index 21f38a78..39d4c85c 100644
--- a/main/como/proteomics_preprocessing.py
+++ b/main/como/proteomics_preprocessing.py
@@ -29,7 +29,15 @@ class ZResult:
 
 
 def z_score_calc(abundance: pd.DataFrame, min_thresh: int) -> ZResult:
-    """Calculate Z-scores for protein abundance data."""
+    """Calculate Z-scores for protein abundance data.
+
+    Arg:
+        abundance: DataFrame with protein abundance data.
+        min_thresh: Minimum threshold for abundance values to be considered.
+
+    Returns:
+        A ZResult object containing Z-score transformed data and related statistics.
+    """
     values = abundance.values.copy() + 1
     log_abundance_filt = np.log2(values[values > min_thresh]).reshape((len(abundance), len(abundance.columns)))
     log_abundance = np.log2(values)
@@ -70,7 +78,17 @@ def z_score_calc(abundance: pd.DataFrame, min_thresh: int) -> ZResult:
 
 
 def lighten_color(red: int, green: int, blue: int, factor: float = 0.5) -> str:
-    """Lighten a color by a given factor."""
+    """Lighten a color by a given factor.
+
+    Args:
+        red: Red component (0-255).
+        green: Green component (0-255).
+        blue: Blue component (0-255).
+        factor: Factor by which to lighten the color (0.0 to 1.0).
+
+    Returns:
+        A string representing the lightened color in RGB format.
+    """
     # Convert RGB to HLS
     hue, lightness, saturation = colorsys.rgb_to_hls(red / 255.0, green / 255.0, blue / 255.0)
 
@@ -84,7 +102,17 @@ def lighten_color(red: int, green: int, blue: int, factor: float = 0.5) -> str:
 
 # Plotting function
 def plot_gaussian_fit(z_results: ZResult, facet_titles: bool = True, x_min: int = -4) -> go.Figure:
-    """Plot Gaussian fit for Z-score transformed data."""
+    """Plot Gaussian fit for Z-score transformed data.
+
+    Arg:
+        z_results: The results from the Z-score calculation.
+        facet_titles: Whether to show titles for each facet.
+        x_min: Minimum x-axis value for the plots.
+
+    Returns:
+        A Plotly Figure object containing the Gaussian fit plots.
+
+    """
     zfpkm = z_results.zfpkm
     x_range = z_results.x_range
     density = z_results.density
diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 6df3221f..0effe2e4 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -4,12 +4,13 @@
 import sys
 import time
 from collections import namedtuple
+from collections.abc import Callable
 from concurrent.futures import Future, ProcessPoolExecutor, as_completed
 from dataclasses import dataclass, field
 from enum import Enum
 from io import TextIOWrapper
 from pathlib import Path
-from typing import Callable, NamedTuple
+from typing import NamedTuple
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -131,9 +132,12 @@ def genefilter(data: pd.DataFrame | npt.NDArray, filter_func: Callable[[npt.NDAr
 
     This code is based on the `genefilter` function found in R's `genefilter` package: https://www.rdocumentation.org/packages/genefilter/versions/1.54.2/topics/genefilter
 
-    :param data: The data to filter
-    :param filter_func: THe function to filter the data by
-    :return: A NumPy array of the filtered data.
+    Arg:
+        data: The data to filter, either a Pandas DataFrame or a NumPy array.
+        filter_func: THe function to filter the data by
+
+    Returns:
+        A NumPy array of the filtered data.
     """
     if not isinstance(data, (pd.DataFrame, npt.NDArray)):
         _log_and_raise_error(
@@ -154,10 +158,13 @@ async def _build_matrix_results(
 ) -> _ReadMatrixResults:
     """Read the counts matrix and returns the results.
 
-    :param matrix: The gene counts matrix to process
-    :param metadata_df: The configuration dataframe related to the current context
-    :param taxon: The NCBI Taxon ID
-    :return: A dataclass `ReadMatrixResults`
+    Arg:
+        matrix: The gene counts matrix to process
+        metadata_df: The configuration dataframe related to the current context
+        taxon: The NCBI Taxon ID
+
+    Returns:
+        A dataclass `ReadMatrixResults`
     """
     matrix.dropna(subset="ensembl_gene_id", inplace=True)
     conversion = await ensembl_to_gene_id_and_symbol(ids=matrix["ensembl_gene_id"].tolist(), taxon=taxon)
@@ -226,7 +233,14 @@ async def _build_matrix_results(
 
 
 def calculate_tpm(metrics: NamedMetrics) -> NamedMetrics:
-    """Calculate the Transcripts Per Million (TPM) for each sample in the metrics dictionary."""
+    """Calculate the Transcripts Per Million (TPM) for each sample in the metrics dictionary.
+
+    Args:
+        metrics: A dictionary of study metrics to calculate TPM for.
+
+    Returns:
+        A dictionary of study metrics with TPM calculated.
+    """
     for sample in metrics:
         count_matrix = metrics[sample].count_matrix
 
@@ -242,7 +256,15 @@ def calculate_tpm(metrics: NamedMetrics) -> NamedMetrics:
 
 
 def _calculate_fpkm(metrics: NamedMetrics, scale: int = 1e6) -> NamedMetrics:
-    """Calculate the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) for each sample in the metrics dictionary."""
+    """Calculate the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) for each sample in the metrics dictionary.
+
+    Args:
+        metrics: A dictionary of study metrics to calculate FPKM for.
+        scale: The scaling factor for normalization (default is 1e6).
+
+    Returns:
+        A dictionary of study metrics with FPKM calculated.
+    """
     for study in metrics:
         matrix_values = []
 
@@ -333,7 +355,7 @@ def _zfpkm_calculation(
          - Standard deviation is estimated based on the assumption that the right tail of the distribution
             This represents expressed genes) can be approximated by a half-normal distribution
 
-     zFPKM Transformation
+    zFPKM Transformation
         - Centers disbribution around 0 and scales it by the standard deviation.
             This makes it easier to compare gene expression across different samples
         - Represents the number of standard deviations away from the mean of the "inactive" gene distribution
@@ -342,6 +364,19 @@ def _zfpkm_calculation(
         - Research shows that a zFPKM value of -3 or greater can be used as
             a threshold for calling a gene as "active" and/or "expressed"
             : https://doi.org/10.1186/1471-2164-14-778
+
+    Args:
+        column: A Pandas Series containing FPKM values for a single sample.
+        peak_parameters: Parameters for peak identification in zFPKM calculation.
+        bandwidth: The bandwidth for kernel density estimation in zFPKM calculation.
+
+    Returns:
+        A named tuple containing:
+            - zfpkm: A Pandas Series of zFPKM values for the input sample.
+            - density: A named tuple containing the x and y values of the KDE.
+            - mu: The mean of the "inactive" gene distribution.
+            - std_dev: The estimated standard deviation of the "inactive" gene distribution.
+            - max_fpkm: The maximum FPKM value at the identified peak.
     """
     values: npt.NDArray = column.values
     # replace na values with 0
@@ -374,7 +409,19 @@ def zfpkm_transform(
     bandwidth: int,
     update_every_percent: float = 0.1,
 ) -> tuple[dict[str, _ZFPKMResult], DataFrame]:
-    """Perform zFPKM calculation/transformation."""
+    """Perform zFPKM calculation/transformation.
+
+    Args:
+        fpkm_df: A DataFrame containing FPKM values with genes as rows and samples as columns.
+        peak_parameters: Parameters for peak identification in zFPKM calculation.
+        bandwidth: The bandwidth for kernel density estimation in zFPKM calculation.
+        update_every_percent: Frequency of progress updates as a decimal between 0 and 1 (e.g., 0.1 for every 10%).
+
+    Returns:
+        A tuple containing:
+            - A dictionary of intermediate results for each sample.
+            - A DataFrame of zFPKM values with the same shape as the input fpkm_df.
+    """
     if update_every_percent > 1:
         logger.warning(f"update_every_percent should be a decimal value between 0 and 1; got: {update_every_percent} - will convert to percentage")
         update_every_percent /= 100
@@ -459,9 +506,11 @@ def zfpkm_plot(results, *, output_png_filepath: Path, plot_xfloor: int = -4):
     mega_df.columns = pd.Series(data=["sample_name", "log2fpkm", "fpkm_density", "fitted_density_scaled"])
     mega_df = mega_df.melt(id_vars=["log2fpkm", "sample_name"], var_name="source", value_name="density")
 
+    fig: plt.Figure
+    axes: plt.Axes
     fig, axes = plt.subplots(nrows=len(results), ncols=1, figsize=(8, 4 * len(results)))
     if len(results) == 1:
-        axes = [axes]
+        axes: list[plt.Axes] = [axes]
 
     for i, sample_name in enumerate(results):
         sample_data = mega_df[mega_df["sample_name"] == sample_name]
@@ -476,15 +525,22 @@ def zfpkm_plot(results, *, output_png_filepath: Path, plot_xfloor: int = -4):
         axis.set_ylabel("density [scaled]")
         axis.legend(title="Source")
 
-    plt.tight_layout()
+    fig.tight_layout()
     if output_png_filepath.suffix != ".png":
         logger.warning(f"Output filepath did not end in '.png', setting to '.png' now. Got: '{output_png_filepath.suffix}'")
         output_png_filepath = output_png_filepath.with_suffix(".png")
-    plt.savefig(output_png_filepath)
+    fig.savefig(output_png_filepath)
 
 
 def calculate_z_score(metrics: NamedMetrics) -> NamedMetrics:
-    """Calculate the z-score for each sample in the metrics dictionary."""
+    """Calculate the z-score for each sample in the metrics dictionary.
+
+    Args:
+        metrics: A dictionary of study metrics to calculate z-scores for.
+
+    Returns:
+        A dictionary of study metrics with z-scores calculated.
+    """
     for sample in metrics:
         log_matrix = np.log(metrics[sample].normalization_matrix)
         z_matrix = pd.DataFrame(data=sklearn.preprocessing.scale(log_matrix, axis=1), columns=metrics[sample].sample_names)
@@ -499,7 +555,17 @@ def cpm_filter(
     filtering_options: _FilteringOptions,
     prep: RNAType,
 ) -> NamedMetrics:
-    """Apply Counts Per Million (CPM) filtering to the count matrix for a given sample."""
+    """Apply Counts Per Million (CPM) filtering to the count matrix for a given sample.
+
+    Args:
+        context_name: The name of the context being processed.
+        metrics: A dictionary of study metrics to filter.
+        filtering_options: Options for filtering the count matrix.
+        prep: The RNA preparation type.
+
+    Returns:
+        A dictionary of filtered study metrics.
+    """
     config = Config()
     n_exp = filtering_options.replicate_ratio
     n_top = filtering_options.high_replicate_ratio
@@ -543,7 +609,15 @@ def cpm_filter(
 
 
 def tpm_quantile_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions) -> NamedMetrics:
-    """Apply quantile-based filtering to the TPM matrix for a given sample."""
+    """Apply quantile-based filtering to the TPM matrix for a given sample.
+
+    Args:
+        metrics: A dictionary of study metrics to filter.
+        filtering_options: Options for filtering the count matrix.
+
+    Returns:
+        A dictionary of filtered study metrics.
+    """
     # TODO: Write the TPM matrix to disk
 
     n_exp = filtering_options.replicate_ratio
@@ -572,13 +646,13 @@ def tpm_quantile_filter(*, metrics: NamedMetrics, filtering_options: _FilteringO
         top_genes: npt.NDArray[bool] = genefilter(boolean_expression, top_func)
 
         # Only keep `entrez_gene_ids` that pass `min_genes`
-        metric.entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, min_genes) if keep]
-        metric.gene_sizes = np.array(gene for gene, keep in zip(gene_size, min_genes) if keep)
+        metric.entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, min_genes, strict=True) if keep]
+        metric.gene_sizes = np.array(gene for gene, keep in zip(gene_size, min_genes, strict=True) if keep)
         metric.count_matrix = metric.count_matrix.iloc[min_genes, :]
         metric.normalization_matrix = metrics[sample].normalization_matrix.iloc[min_genes, :]
 
-        keep_top_genes = [gene for gene, keep in zip(entrez_ids, top_genes) if keep]
-        metric.high_confidence_entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, keep_top_genes) if keep]
+        keep_top_genes = [gene for gene, keep in zip(entrez_ids, top_genes, strict=True) if keep]
+        metric.high_confidence_entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, keep_top_genes, strict=True) if keep]
 
     metrics = calculate_z_score(metrics)
 
@@ -595,7 +669,20 @@ def zfpkm_filter(
     bandwidth: int,
     output_png_filepath: Path | None,
 ) -> NamedMetrics:
-    """Apply zFPKM filtering to the FPKM matrix for a given sample."""
+    """Apply zFPKM filtering to the FPKM matrix for a given sample.
+
+    Args:
+        metrics: A dictionary of study metrics to filter.
+        filtering_options: Options for filtering the count matrix.
+        calculate_fpkm: Whether to calculate FPKM from counts.
+        force_zfpkm_plot: Whether to force plotting of zFPKM results even if there are many samples.
+        peak_parameters: Parameters for peak identification in zFPKM calculation.
+        bandwidth: The bandwidth for kernel density estimation in zFPKM calculation.
+        output_png_filepath: Optional filepath to save the zFPKM plot.
+
+    Returns:
+        A dictionary of filtered study metrics.
+    """
     min_sample_expression = filtering_options.replicate_ratio
     high_confidence_sample_expression = filtering_options.high_replicate_ratio
     cut_off = filtering_options.cut_off
@@ -628,13 +715,13 @@ def zfpkm_filter(
         min_samples = round(min_sample_expression * len(zfpkm_df.columns))
         min_func = k_over_a(min_samples, cut_off)
         min_genes: npt.NDArray[bool] = genefilter(zfpkm_df, min_func)
-        metric.entrez_gene_ids = [gene for gene, keep in zip(metric.entrez_gene_ids, min_genes) if keep]
+        metric.entrez_gene_ids = [gene for gene, keep in zip(metric.entrez_gene_ids, min_genes, strict=True) if keep]
 
         # determine which genes are confidently expressed
         top_samples = round(high_confidence_sample_expression * len(zfpkm_df.columns))
         top_func = k_over_a(top_samples, cut_off)
         top_genes: npt.NDArray[bool] = genefilter(zfpkm_df, top_func)
-        metric.high_confidence_entrez_gene_ids = [gene for gene, keep in zip(metric.entrez_gene_ids, top_genes) if keep]
+        metric.high_confidence_entrez_gene_ids = [gene for gene, keep in zip(metric.entrez_gene_ids, top_genes, strict=True) if keep]
 
     return metrics
 
@@ -651,7 +738,22 @@ def filter_counts(
     bandwidth: int,
     output_png_filepath: Path | None = None,
 ) -> NamedMetrics:
-    """Filter the count matrix based on the specified technique."""
+    """Filter the count matrix based on the specified technique.
+
+    Args:
+        context_name: The name of the context being processed.
+        metrics: A dictionary of study metrics to filter.
+        technique: The filtering technique to use.
+        filtering_options: Options for filtering the count matrix.
+        prep: The RNA preparation type.
+        force_zfpkm_plot: Whether to force plotting of zFPKM results even if there are many samples.
+        peak_parameters: Parameters for peak identification in zFPKM calculation.
+        bandwidth: The bandwidth for kernel density estimation in zFPKM calculation.
+        output_png_filepath: Optional filepath to save the zFPKM plot.
+
+    Returns:
+        A dictionary of filtered study metrics.
+    """
     match technique:
         case FilteringTechnique.CPM:
             return cpm_filter(context_name=context_name, metrics=metrics, filtering_options=filtering_options, prep=prep)
diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 8a843c4b..2a219b5d 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -196,7 +196,7 @@ def _organize_gene_counts_files(data_dir: Path) -> list[_StudyMetrics]:
 
     # For each study, collect gene count files, fragment files, insert size files, layouts, and strandedness information
     study_metrics: list[_StudyMetrics] = []
-    for gene_dir, strand_dir in zip(gene_counts_directories, strandedness_directories):
+    for gene_dir, strand_dir in zip(gene_counts_directories, strandedness_directories, strict=True):
         count_files = list(gene_dir.glob("*.tab"))
         strand_files = list(strand_dir.glob("*.txt"))
         if len(count_files) == 0:
@@ -328,7 +328,17 @@ async def _write_counts_matrix(
     output_counts_matrix_filepath: Path,
     rna: RNAType,
 ) -> pd.DataFrame:
-    """Create a counts matrix file by reading gene counts table(s)."""
+    """Create a counts matrix file by reading gene counts table(s).
+
+    Args:
+        config_df: Configuration DataFrame containing sample information.
+        como_context_dir: Path to the COMO_input directory containing gene count files.
+        output_counts_matrix_filepath: Path where the output counts matrix CSV will be saved.
+        rna: RNAType enum indicating whether to process 'trna' or 'mrna' samples.
+
+    Returns:
+        A pandas DataFrame representing the final counts matrix.
+    """
     study_metrics = _organize_gene_counts_files(data_dir=como_context_dir)
     counts: list[pd.DataFrame] = await asyncio.gather(*[_create_sample_counts_matrix(metric) for metric in study_metrics])
     rna_specific_sample_names = set(config_df.loc[config_df["library_prep"] == rna.value, "sample_name"].tolist())
@@ -358,7 +368,20 @@ async def _create_config_df(  # noqa: C901
     """Create configuration sheet.
 
     The configuration file created is based on the gene counts matrix.
-     If using zFPKM normalization technique, mean fragment lengths will be fetched
+    If using zFPKM normalization technique, mean fragment lengths will be fetched
+
+    Args:
+        context_name: Name of the context, used as a prefix for sample names.
+        como_context_dir: Path to the COMO_input directory containing subdirectories for
+            gene counts, layouts, strandedness, fragment sizes, and prep methods.
+        gene_count_dirname: Name of the subdirectory containing gene count files.
+        layout_dirname: Name of the subdirectory containing layout files.
+        strandedness_dirname: Name of the subdirectory containing strandedness files.
+        fragment_sizes_dirname: Name of the subdirectory containing fragment size files.
+        prep_method_dirname: Name of the subdirectory containing library preparation method files.
+
+    Returns:
+        A pandas DataFrame representing the configuration sheet.
     """
     label_regex: Final = re.compile(r"(?P<study>S\d{1,3})(?P<rep>R\d{1,3})(?P<run>r\d{1,3})?")
     gene_counts: list[Path] = list((como_context_dir / gene_count_dirname).rglob("*.tab"))
diff --git a/main/como/utils.py b/main/como/utils.py
index 3a3d37ce..27d18c23 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import asyncio
 import contextlib
 import io
 import sys
@@ -8,6 +9,7 @@
 from pathlib import Path
 from typing import Literal
 
+import aiofiles
 import numpy.typing as npt
 import pandas as pd
 import scanpy as sc
@@ -33,7 +35,11 @@ def stringlist_to_list(stringlist: str | list[str]) -> list[str]:
     If '[' and ']' are present in the first and last items of the list,
         assume we are using the "old" method of providing context names
 
-    :param stringlist: The "string list" gathered from the command line. Example input: "['mat', 'xml', 'json']"
+    Args:
+        stringlist: The "string list" gathered from the command line. Example input: "['mat', 'xml', 'json']"
+
+    Returns:
+        A list of strings. Example output: ['mat', 'xml', 'json']
     """
     if isinstance(stringlist, list):
         return stringlist
@@ -66,11 +72,14 @@ def split_gene_expression_data(
 ):
     """Split the gene expression data into single-gene and multiple-gene names.
 
-    :param expression_data: The gene expression data to map
-    :param identifier_column: The column containing the gene identifiers, either 'ensembl_gene_id' or 'entrez_gene_id'
-    :param recon_algorithm: The recon algorithm used to generate the gene expression data
-    :param ensembl_as_index: Should the 'ensembl_gene_id' column be set as the index
-    :return:
+    Arg:
+        expression_data: The gene expression data to map
+        identifier_column: The column containing the gene identifiers, either 'ensembl_gene_id'
+        recon_algorithm: The recon algorithm used to generate the gene expression data
+        ensembl_as_index: Should the 'ensembl_gene_id' column be set as
+
+    Returns:
+        A pandas DataFrame with the split gene expression data
     """
     expression_data.columns = [c.lower() for c in expression_data.columns]
     if recon_algorithm in {Algorithm.IMAT, Algorithm.TINIT}:
@@ -108,11 +117,14 @@ async def _format_determination(
 ) -> pd.DataFrame:
     """Determine the data type of the given input values (i.e., Entrez Gene ID, Gene Symbol, etc.).
 
-    :param biodbnet: The BioDBNet to use for deter
-    :param requested_output: The data type to generate (of type `Output`)
-    :param input_values: The input values to determine
-    :param taxon: The Taxon ID
-    :return: A pandas DataFrame
+    Args:
+        biodbnet: The BioDBNet to use for determination
+        requested_output: The data type to generate (of type `Output`)
+        input_values: The input values to determine
+        taxon: The Taxon ID
+
+    Returns:
+        A pandas DataFrame
     """
     requested_output = [requested_output] if isinstance(requested_output, Output) else requested_output
     coercion = (await biodbnet.db_find(values=input_values, output_db=requested_output, taxon=taxon)).drop(columns=["Input Type"])
@@ -131,10 +143,13 @@ async def _read_file(
     None may be provided to this function so that `asyncio.gather` can safely be used on all sources
         (trna, mrna, scrna, proteomics) without needing to check if the user has provided those sources
 
-    :param path: The path to read from
-    :param kwargs: Additional arguments to pass to pandas.read_csv, pandas.read_excel,
-        or scanpy.read_h5ad, depending on the filepath provided
-    :return: None, or a pandas DataFrame or AnnData
+    Args:
+        path: The path to read from
+        h5ad_as_df: If True and the file is an h5ad, return a DataFrame of the .X matrix instead of an AnnData object
+        kwargs: Additional arguments to pass to pandas.read_csv, pandas.read_excel, or scanpy.read_h5ad, depending on the filepath provided
+
+    Returns:
+        None, or a pandas DataFrame or AnnData
     """
     if isinstance(path, (pd.DataFrame, sc.AnnData)):
         return path
@@ -152,13 +167,13 @@ async def _read_file(
         case ".csv" | ".tsv" | ".txt":
             if "sep" not in kwargs:
                 kwargs.setdefault("sep", "," if path.suffix == ".csv" else "\t")
-            with path.open("r") as i_stream:
-                content = i_stream.read()
+            async with aiofiles.open(path) as i_stream:
+                content = await i_stream.read()
                 return pd.read_csv(io.StringIO(content), **kwargs)
         case ".xlsx" | ".xls":
-            return pd.read_excel(path, **kwargs)
+            return await asyncio.to_thread(pd.read_excel, path, **kwargs)
         case ".h5ad":
-            adata: sc.AnnData = sc.read_h5ad(path, **kwargs)
+            adata: sc.AnnData = await asyncio.to_thread(sc.read_h5ad, path, **kwargs)
             if h5ad_as_df:
                 df = adata.to_df().T
                 df.index.name = "gene_symbol"
@@ -200,7 +215,14 @@ async def get_missing_gene_data(values: list[str] | pd.DataFrame, taxon_id: int
 
 
 def _listify(value):
-    """Convert items into a list."""
+    """Convert items into a list.
+
+    Args:
+        value: The value to convert to a list
+
+    Returns:
+        A list containing `value`, unless it is already a list
+    """
     return [value] if not isinstance(value, list) else value
 
 
diff --git a/ruff.toml b/ruff.toml
index 309140fa..509a1911 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -1,6 +1,7 @@
 line-length = 150
 extend-include = ["docs/**/*.py", "tests/**/*.py", "**/*.ipynb"]
 
+
 [format]
 quote-style = "double"
 docstring-code-format = true
@@ -41,6 +42,9 @@ ignore = [
     "F401",     # allow unused imports
 ]
 
+[lint.pydocstyle]
+convention = "google"
+
 [lint.per-file-ignores]
 "tests/*" = [
     "D101",     # allow undocumented public class
diff --git a/tests/unit/test_data_types.py b/tests/unit/test_data_types.py
index d5e1277a..f5023e08 100644
--- a/tests/unit/test_data_types.py
+++ b/tests/unit/test_data_types.py
@@ -4,7 +4,7 @@
 def test_source_types():
     """Validate that source types always go in the order of 'trna', 'mrna', 'scrna', 'proteomics'."""
     expected_order = ["trna", "mrna", "scrna", "proteomics"]
-    for expected, source in zip(expected_order, SourceTypes):
+    for expected, source in zip(expected_order, SourceTypes, strict=True):
         expected: str
         source: SourceTypes
         assert expected == source.value
diff --git a/tests/unit/test_rnaseq_preprocess.py b/tests/unit/test_rnaseq_preprocess.py
index c0d49c2e..bf834aea 100644
--- a/tests/unit/test_rnaseq_preprocess.py
+++ b/tests/unit/test_rnaseq_preprocess.py
@@ -40,7 +40,7 @@ async def test_build_from_tab_valid_file(self):
     @pytest.mark.asyncio
     async def test_build_from_tab_invalid_file(self):
         """Validate error on invalid file."""
-        with pytest.raises(ValueError, match="Building STAR information requires a '.tab' file"):
+        with pytest.raises(ValueError, match="Building STAR information requires a '\.tab' file"):
             await _STARinformation.build_from_tab(TestSTARInformation.invalid_data)
 
 

From fc4ee6e8b67ec3ce03b473776c7d99ab5a50374a Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Thu, 2 Oct 2025 11:30:10 -0500
Subject: [PATCH 328/345] Fix ruff linting errors (#218)

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: mumo-dev <samuelmumo@Samuels-MacBook-Pro.local>
---
 .github/workflows/continuous_integration.yml  |   12 +-
 .github/workflows/pypackage_build.yml         |   92 +
 main/COMO.ipynb                               |  161 +-
 main/como/create_context_specific_model.py    |    9 +-
 main/como/merge_xomics.py                     |    2 +-
 main/como/proteomics/proteomics_preprocess.py |    4 +-
 main/como/proteomics_preprocessing.py         |    2 +-
 main/como/rnaseq_gen.py                       |   33 +-
 main/como/rnaseq_preprocess.py                |   17 +-
 pyproject.toml                                |   28 +-
 uv.lock                                       | 3367 +++++++++--------
 11 files changed, 2034 insertions(+), 1693 deletions(-)
 create mode 100644 .github/workflows/pypackage_build.yml

diff --git a/.github/workflows/continuous_integration.yml b/.github/workflows/continuous_integration.yml
index 113c905d..bdc7b156 100644
--- a/.github/workflows/continuous_integration.yml
+++ b/.github/workflows/continuous_integration.yml
@@ -14,7 +14,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v4
+        uses: astral-sh/setup-uv@v5
 
       - name: Create Virtual Environment
         run: uv venv
@@ -26,17 +26,17 @@ jobs:
         run: uv run jupyter nbconvert --clear-output --inplace "main/COMO.ipynb"
 
       - name: Format Python Imports
-        uses: astral-sh/ruff-action@v2
+        uses: astral-sh/ruff-action@v3
         with:
           args: "check --fix --select I"
 
       - name: Format code
-        uses: astral-sh/ruff-action@v2
+        uses: astral-sh/ruff-action@v3
         with:
           args: "format"
 
       - name: Format Notebook
-        uses: astral-sh/ruff-action@v2
+        uses: astral-sh/ruff-action@v3
         with:
           args: "format main/COMO.ipynb"
 
@@ -54,7 +54,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Check Lint
-        uses: astral-sh/ruff-action@v2
+        uses: astral-sh/ruff-action@v3
         with:
           args: "check --no-fix --verbose"
 
@@ -68,7 +68,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Install uv
-        uses: astral-sh/setup-uv@v4
+        uses: astral-sh/setup-uv@v5
         with:
           enable-cache: "true"
           cache-suffix: "${{ matrix.python-version }}"
diff --git a/.github/workflows/pypackage_build.yml b/.github/workflows/pypackage_build.yml
new file mode 100644
index 00000000..3570f7de
--- /dev/null
+++ b/.github/workflows/pypackage_build.yml
@@ -0,0 +1,92 @@
+name: Build and publish python package
+
+on:
+  push:
+    tags:
+      - "*"
+
+# Define permissions needed for the workflow GITHUB_TOKEN
+permissions:
+  contents: read # Allow checkout
+  packages: write # Allow publishing to GitHub Packages
+
+
+jobs:
+  build:
+    strategy:
+      matrix:
+        operating-system: [macos-latest, ubuntu-latest, windows-latest]
+        python-version: [ 3.10, 3.11, 3.12, 3.13 ]
+
+    name: Build Python Package (${{ matrix.operating-system }}, Python ${{ matrix.python-version }})
+    runs-on: ${{ matrix.operating-system }}
+
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v4
+        with:
+          ref: ${{ github.ref }}
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install Hatch
+        run: pip install hatch
+
+      - name: Build distributions
+        run: hatch build # Uses pyproject.toml to build sdist and wheel into dist/
+
+      - name: Upload distributions artifact
+        uses: actions/upload-artifact@v4 # Action to save artifacts between jobs
+        with:
+          name: como-distribution-package-${{ matrix.operating-system }}-${{ matrix.python-version }}  # Name for the artifact
+          path: dist/ # Path to the directory to upload
+
+  publish:
+    strategy:
+      matrix:
+        python-version: [ 3.10, 3.11, 3.12, 3.13 ]
+        operating-system: [macos-latest, windows-latest, ubuntu-latest]
+
+    name: Publish to GitHub Packages
+    runs-on: ubuntu-latest
+    needs: build # Depends on the build job succeeding
+
+    # IMPORTANT: Only run the publish job when a tag starting with 'v' is pushed
+    if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v')
+
+
+    permissions: # Explicit permissions needed for this job
+      packages: write # Required to write to GitHub Packages registry
+      contents: read # Needed if accessing repo content (e.g., for download artifact)
+
+    steps:
+      - name: Download distributions artifact
+        uses: actions/download-artifact@v4 # Action to retrieve artifacts from previous job.
+        with:
+          name: como-distribution-package-${{ matrix.operating-system }}-${{ matrix.python-version }}
+          path: dist/
+
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install Twine
+        run: pip install twine
+
+      - name: Publish package to GitHub Packages
+        env:
+          # Use __token__ as username and the automatically generated GITHUB_TOKEN as password
+          TWINE_USERNAME: __token__
+          TWINE_PASSWORD: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          echo "Uploading to GitHub Packages for repository: ${{ github.repository }}"
+          # Construct the repository URL dynamically using the repository owner
+          TWINE_REPOSITORY_URL="https://pypi.pkg.github.com/${{ github.repository_owner }}"
+          python -m twine upload --verbose --repository-url ${TWINE_REPOSITORY_URL} dist/*
+
+        
+    
diff --git a/main/COMO.ipynb b/main/COMO.ipynb
index 3cbebec1..6b5b7f51 100644
--- a/main/COMO.ipynb
+++ b/main/COMO.ipynb
@@ -1,12 +1,8 @@
 {
  "cells": [
   {
+   "metadata": {},
    "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
    "source": [
     "# COMO: Constraint-based Optomization of Metabolic Objectives\n",
     "\n",
@@ -56,15 +52,12 @@
     "    2. Knock-out simulation\n",
     "    3. Compare results between perturbed and unperturbed models (i.e., knocked-out models vs non-knocked-out models)\n",
     "    4. Integrate with disease genes and create a score of drug targets"
-   ]
+   ],
+   "id": "ea1c9614518c30bc"
   },
   {
+   "metadata": {},
    "cell_type": "markdown",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
    "source": [
     "# Step 1: Data Preprocessing and Analysis\n",
     "\n",
@@ -229,7 +222,8 @@
     "        </tr>\n",
     "    </tbody>\n",
     "</table>"
-   ]
+   ],
+   "id": "28d59417491e63db"
   },
   {
    "metadata": {},
@@ -259,18 +253,20 @@
     "# If you are using single-cell data, adjust the following lines accordingly\n",
     "scrna_matrix_filepath = {context: Path(f\"data/results/{context}/scrna/scrna_{context}.csv\") for context in context_names}\n",
     "# scrna_matrix_filepath = [Path(f\"data/results/{context}/scrna/scrna_{context}.h5ad\") for context in context_names]\n"
-   ]
+   ],
+   "id": "18061578964ca096"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "### RNA-seq Preprocessing Parameters\n",
     "- `context_names`: The tissue/cell types to use. This is a simple space-separated list of items, such as \"naiveB regulatoryTcell\"\n",
     "- `gene_format`: The format of input genes, accepts `\"Extrez\"`, `\"Emsembl\"` or `\"Symbol\"`\n",
     "- `taxon_id`: The [NCBI Taxon ID](https://www.ncbi.nlm.nih.gov/taxonomy) to use\n",
     "- `preprocess_mode`: This should be set to `\"create-matrix\"` if you are **not** providing a matrix, otherwise set it to `\"provide-matrix\"`"
-   ]
+   ],
+   "id": "b475e41b000d0cb5"
   },
   {
    "metadata": {},
@@ -294,11 +290,12 @@
     "        cache=True,\n",
     "        log_level=\"INFO\",\n",
     "    )"
-   ]
+   ],
+   "id": "dbfb32f2a0f7b8ef"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "\n",
     "## Identification of Gene Activity in Transcriptomic and Proteomic Datasets\n",
@@ -326,11 +323,12 @@
     "This is a flat cutoff value of counts per million normalized values. Gene expression will be checked for consensus at the replicate and study level.\n",
     "\n",
     "This method is not recommended, as zFPKM is much more robust for a similar level of \"hands-off\" model building\n"
-   ]
+   ],
+   "id": "a820a3985a2601c7"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "### RNA Sequencing Analysis\n",
     "#### Bulk RNA Sequencing\n",
@@ -346,11 +344,12 @@
     "\n",
     "#### Single Cell RNA Sequencing\n",
     "While the Snakemake pipeline does not yet support single-cell alignment, and COMO does not yet support automated configuration file and counts matrix file creation for single-cell alignment output from STAR, it is possible to use single-cell data to create a model with COMO. Because normalization strategies can be applied to single-cell data in the same way it is applied to bulk RNA sequencing, `como/rnaseq_gen.py` can be used with a provided counts matrix and configuration file, from [Step 1](Step-1:-Initialize-and-Preprocess-RNA-seq-data), above. Just like `\"total\"` and `\"mRNA\"`, `como/rnaseq_gen.py` can be executed with `\"SC\"` as the \"`--library-prep`\" argument to help COMO differentiate it from any bulk RNA sequencing data if multiple strategies are being used."
-   ]
+   ],
+   "id": "a1d6608d045c7399"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "\n",
     "### Total RNA Sequencing Generation\n",
@@ -364,7 +363,8 @@
     "- `quantile`: The cutoff Transcripts-Per-Million quantile for filtering\n",
     "- `min_zfpkm`: The cutoff for Counts-Per-Million filtering\n",
     "- `prep_method`: The library method used for preparation. Options are: `\"total\"`, `\"mRNA\"`, or `\"SC\"`,\n"
-   ]
+   ],
+   "id": "1fcf38e9cbc3de48"
   },
   {
    "metadata": {},
@@ -399,11 +399,12 @@
     "        technique=technique,\n",
     "        cutoff=cutoff\n",
     "    )"
-   ]
+   ],
+   "id": "5a2e17b646d6de36"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "## mRNA Sequencing Generation\n",
     "These parameters are identical to the ones listed for [total RNA sequencing](#Total-RNA-Sequencing-Generation), but they are listed again here for ease of reference\n",
@@ -418,7 +419,8 @@
     "- `quantile`: The cutoff Transcripts-Per-Million quantile for filtering\n",
     "- `min_zfpkm`: The cutoff for Counts-Per-Million filtering\n",
     "- `prep_method`: The library method used for preparation. Options are: `\"total\"`, `\"mRNA\"`, or `\"SC\"`,\n"
-   ]
+   ],
+   "id": "87b5397301541650"
   },
   {
    "metadata": {},
@@ -453,11 +455,12 @@
     "        technique=technique,\n",
     "        cutoff=cutoff\n",
     "    )"
-   ]
+   ],
+   "id": "3e73c07fa4a9e49a"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "## Single-Cell RNA Sequencing Generation\n",
     "These parameters are identical to the ones listed for [total RNA sequencing](#Total-RNA-Sequencing-Generation), but they are listed again here for ease of reference\n",
@@ -472,7 +475,8 @@
     "- `quantile`: The cutoff Transcripts-Per-Million quantile for filtering\n",
     "- `min_zfpkm`: The cutoff for Counts-Per-Million filtering\n",
     "- `prep_method`: The library method used for preparation. Options are: `\"total\"`, `\"mRNA\"`, or `\"scrna\"`,\n"
-   ]
+   ],
+   "id": "ae1278480199f415"
   },
   {
    "metadata": {},
@@ -507,11 +511,12 @@
     "        technique=technique,\n",
     "        cutoff=cutoff\n",
     "    )"
-   ]
+   ],
+   "id": "567734d827571d6f"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "## Proteomics Analysis\n",
     "The parameters here are mostly the same to total RNA and mRNA sequencing analysis, and are listed here for easier reference\n",
@@ -523,7 +528,8 @@
     "- `high_rep_ratio`: The ratio required before a gene is considered \"high-confidence\" in the replicate\n",
     "- `high_batch_ratio`: The ratio required before a gene is considered \"high-confidence\" in the study\n",
     "- `quantile`: The cutoff Transcripts-Per-Million quantile for filtering"
-   ]
+   ],
+   "id": "ea7004d23807dd1"
   },
   {
    "metadata": {},
@@ -549,7 +555,8 @@
     "        high_confidence_batch_ratio=0.7,\n",
     "        quantile=25,\n",
     "    )"
-   ]
+   ],
+   "id": "a7715d2cf9b0191a"
   },
   {
    "metadata": {},
@@ -574,7 +581,8 @@
     "- `n_neighbors_batch`: N nearest neighbors for batch clustering. The default is `\"default\"`, which is the total number of batches\n",
     "- `n_neighbors_context`: N nearest neighbors for context clustering. The default is `\"default\"`, which is the total number of contexts\n",
     "- `seed`: The random seed for clustering algorithm initialization. If not specified, `np.random.randint(0, 100000)` is used"
-   ]
+   ],
+   "id": "f4f395e25eeb1185"
   },
   {
    "metadata": {},
@@ -617,11 +625,12 @@
     "# fmt: on\n",
     "\n",
     "!{cmd}"
-   ]
+   ],
+   "id": "cc61326b8df01fc4"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "## Merge Expression from Different Data Sources\n",
     "\n",
@@ -656,13 +665,14 @@
     "- `proteomics_weight`: Proteomic weight for merging zFPKM distribution\n",
     "\n",
     "Each of the \"weights\" (`total_rna_weight`, `mrna_weight`, etc.) are used to place a significance on each method. Becuase there are many steps in the Dogma from transcription to translation, the gene expression as seen by total RNA or mRNA sequencing may not be representative of the gene's protein expression, and this its metabolic impact. Because of this, you are able to weight each source more (or less) than another."
-   ]
+   ],
+   "id": "236fb36fa41f60b"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "expression_requirement = 3\n",
     "requirement_adjust = \"regressive\"\n",
@@ -692,11 +702,12 @@
     "# fmt: on\n",
     "\n",
     "!{cmd}"
-   ]
+   ],
+   "id": "3ce78fc277c55885"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "# Step 2: Create Tissue/Cell-Type Specific Models\n",
     "\n",
@@ -758,13 +769,14 @@
     "- `boundary_reactions_filename`: The filename of boundary reactions that should be used\n",
     "- `force_reactions_filename`: The filename of the force reactions to be used. Force reactions will (as the name implies) force the optimizer to use these reactions, **no matter their expression**\n",
     "- `exclude_reactions_filename`: The filename of reactions to exclude from the model, no matter their expression"
-   ]
+   ],
+   "id": "b3a182617ff3eb7a"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "import json\n",
     "import os\n",
@@ -826,11 +838,12 @@
     "        )\n",
     "        # fmt: on\n",
     "        !{cmd}"
-   ]
+   ],
+   "id": "e94c36e65ba46e38"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "# Generate MEMOTE Reports\n",
     "> NOTE: This step is entirely optional\n",
@@ -850,13 +863,14 @@
     "- `mapName`: This is the name of the map file. In the `map_dict` dictionary below, this value would be `trypto`, `retinol`, etc.\n",
     "- `exampleTissue`: This is the name of the tissue context\n",
     "- `ALGORITHM`: This is the algorithm (`recon_algorithm`) used in the above model creation step\n"
-   ]
+   ],
+   "id": "47e3e576b43a040d"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "import cobra\n",
     "from como.project import Config\n",
@@ -926,11 +940,12 @@
     "        )\n",
     "\n",
     "        !{cmd}"
-   ]
+   ],
+   "id": "4a064f3322a3991e"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "# Step 3: Disease-related Gene Identification\n",
     "This step can identify disease related genes by analyzing patient transcriptomics' data\n",
@@ -944,13 +959,14 @@
     "- `disease_names`: The diseases you are using. This should match the first section of the sheet name in the Excel file\n",
     "- `data_source`: The datasource you are using for disease analysis. This should be`\"rnaseq\"`\n",
     "- `taxon_id`: The [NCBI Taxon ID](https://www.ncbi.nlm.nih.gov/taxonomy) to use for disease analysis"
-   ]
+   ],
+   "id": "3aa49b19ab4fc4b9"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "from como.utils import stringlist_to_list\n",
     "\n",
@@ -975,11 +991,12 @@
     "    # fmt: on\n",
     "\n",
     "    !{cmd}"
-   ]
+   ],
+   "id": "7f946d38cac3a18c"
   },
   {
-   "cell_type": "markdown",
    "metadata": {},
+   "cell_type": "markdown",
    "source": [
     "# Step 4: Drug Targets & Repurposable Drug Identification\n",
     "This step performs a series of tasks:\n",
@@ -1009,13 +1026,14 @@
     "Other than the `model_files` parameter (if required), the only other parameter for this section is the `solver` option\n",
     "\n",
     "- `solver`: The solver you would like to use. Available options are `\"gurobi\"` or `\"glpk\"`\n"
-   ]
+   ],
+   "id": "feb9da60acb9a3b3"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "import json\n",
     "\n",
@@ -1080,30 +1098,11 @@
     "\n",
     "            cmd = \" \".join(cmd)\n",
     "            !{cmd}"
-   ]
+   ],
+   "id": "c999b3f953381f9f"
   }
  ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.15"
-  },
-  "toc-autonumbering": true,
-  "toc-showcode": true
- },
+ "metadata": {},
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 5
 }
diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index f6ac8b91..4e483985 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -172,7 +172,7 @@ def _gene_rule_logical(gpr_expression: str, level: int = 0) -> str:
         inner_string = inner_string.replace("[", "")
         inner_string = inner_string.replace("]", "")
 
-    expression_out = f"{gpr_expression[:loc_l]}{inner_string}{gpr_expression[loc_r + 1:]}"
+    expression_out = f"{gpr_expression[:loc_l]}{inner_string}{gpr_expression[loc_r + 1 :]}"
     expression_out = _gene_rule_logical(expression_out, level + 1)
 
     return expression_out
@@ -340,11 +340,11 @@ def _build_with_imat(
 def _build_with_tinit(cobra_model: cobra.Model, s_matrix, lb, ub, expr_vector, solver, idx_force) -> Model:
     properties = tINITProperties(
         reactions_scores=expr_vector,
-        solver=solver,
         essential_reactions=idx_force,
         production_weight=0.0,
         allow_excretion=False,
         no_reverse_loops=True,
+        solver=solver,
     )
     algorithm = tINIT(s_matrix, lb, ub, properties)
     algorithm.preprocessing()
@@ -384,7 +384,7 @@ def _map_expression_to_reaction(
             continue
         for gid in gene_ids:
             if gid in gene_expressions.index:
-                rep_val = f' {gene_expressions.at[gid, "active"]} '
+                rep_val = f" {gene_expressions.at[gid, 'active']} "
             else:
                 rep_val = f" {unknown_val!s} "
             gene_reaction_rule = f" {gene_reaction_rule} "  # pad white space to prevent gene matches inside floats
@@ -565,7 +565,6 @@ def _collect_boundary_reactions(path: Path) -> _BoundaryReactions:
     for column in df.columns:
         if column not in [
             "boundary",
-            "reaction",
             "abbreviation",
             "compartment",
             "minimum reaction rate",
@@ -577,7 +576,7 @@ def _collect_boundary_reactions(path: Path) -> _BoundaryReactions:
             )
 
     reactions: list[str] = [""] * len(df)
-    boundary_type: list[str] = df["reaction"].tolist()
+    boundary_type: list[str] = df["boundary"].tolist()
     reaction_abbreviation: list[str] = df["abbreviation"].tolist()
     reaction_compartment: list[str] = df["compartment"].tolist()
     lower_bound = df["minimum reaction rate"].tolist()
diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 8dc2a6fd..77d2336a 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -194,7 +194,7 @@ def _merge_logical_table(df: pd.DataFrame):
         full_entrez_id_sets.add(new_entrez_id)
 
     entrez_dups_list.extend(i.split(" /// ") for i in full_entrez_id_sets)
-    entrez_dups_dict = dict(zip(full_entrez_id_sets, entrez_dups_list))
+    entrez_dups_dict = dict(zip(full_entrez_id_sets, entrez_dups_list, strict=True))
 
     for merged_entrez_id, entrez_dups_list in entrez_dups_dict.items():
         df["entrez_gene_id"].replace(to_replace=entrez_dups_list, value=merged_entrez_id, inplace=True)
diff --git a/main/como/proteomics/proteomics_preprocess.py b/main/como/proteomics/proteomics_preprocess.py
index 89264ca2..85d78ec2 100644
--- a/main/como/proteomics/proteomics_preprocess.py
+++ b/main/como/proteomics/proteomics_preprocess.py
@@ -158,7 +158,7 @@ def _gather_data(self):
             url_count = 0
 
             # Iterate through the URLs available
-            for url, study in zip(ftp_urls, studies):
+            for url, study in zip(ftp_urls, studies, strict=True):
                 ftp_data: FTPManager.Reader = FTPManager.Reader(
                     root_link=url, file_extensions=self._preferred_extensions
                 )
@@ -168,7 +168,7 @@ def _gather_data(self):
                 url_count += len(urls)
 
                 # Iterate through all files and sizes found for url_##
-                for file, size in zip(urls, sizes):
+                for file, size in zip(urls, sizes, strict=True):
                     self.file_information.append(
                         FileInformation(cell_type=cell_type, download_url=file, file_size=size, study=study)
                     )
diff --git a/main/como/proteomics_preprocessing.py b/main/como/proteomics_preprocessing.py
index 1329406d..bced01b0 100644
--- a/main/como/proteomics_preprocessing.py
+++ b/main/como/proteomics_preprocessing.py
@@ -137,7 +137,7 @@ def protein_transform_main(
 ) -> None:
     """Transform protein abundance data."""
     abundance_df: pd.DataFrame = (
-        pd.read_csv(abundance_df) if isinstance(abundance_df, (str, Path)) else abundance_df.fillna(0)
+        pd.read_csv(abundance_df) if isinstance(abundance_df, str | Path) else abundance_df.fillna(0)
     )
     abundance_df = abundance_df[np.isfinite(abundance_df).all(axis=1)]  # Remove +/- infinity values
     z_transform: ZResult = z_score_calc(abundance_df, min_thresh=0)
diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 364bd4c3..b0079b8c 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -3,13 +3,13 @@
 import math
 import multiprocessing
 import time
-from collections import namedtuple
+from collections import Callable, namedtuple
 from dataclasses import dataclass, field
 from enum import Enum
 from functools import partial
 from multiprocessing.pool import Pool
 from pathlib import Path
-from typing import Callable, NamedTuple
+from typing import NamedTuple
 
 import numpy as np
 import numpy.typing as npt
@@ -349,8 +349,7 @@ def zfpkm_transform(
     """Perform zFPKM calculation/transformation."""
     if update_every_percent > 1:
         logger.warning(
-            f"update_every_percent should be a decimal value between 0 and 1; got: {update_every_percent} - "
-            f"will convert to percentage"
+            f"update_every_percent should be a decimal value between 0 and 1; got: {update_every_percent} - will convert to percentage"
         )
         update_every_percent /= 100
 
@@ -359,8 +358,7 @@ def zfpkm_transform(
     cores = min(multiprocessing.cpu_count() - 2, total)
     logger.debug(f"Processing {total:,} samples through zFPKM transform using {cores} cores")
     logger.debug(
-        f"Will update every {update_per_step:,} steps as this is approximately "
-        f"{update_every_percent:.1%} of {total:,}"
+        f"Will update every {update_per_step:,} steps as this is approximately {update_every_percent:.1%} of {total:,}"
     )
 
     with Pool(processes=cores) as pool:
@@ -392,9 +390,7 @@ def zfpkm_transform(
                 total_time = current_time - start_time
                 formatted = f"{i:,}"
                 logger.debug(
-                    f"Processed {formatted:>{log_padding}} of {total:,} - "
-                    f"chunk took {chunk:.1f} seconds - "
-                    f"running for {total_time:.1f} seconds"
+                    f"Processed {formatted:>{log_padding}} of {total:,} - chunk took {chunk:.1f} seconds - running for {total_time:.1f} seconds"
                 )
                 chunk_time = current_time
     return results, zfpkm_df
@@ -546,13 +542,15 @@ def tpm_quantile_filter(*, metrics: NamedMetrics, filtering_options: _FilteringO
         top_genes: npt.NDArray[bool] = genefilter(boolean_expression, top_func)
 
         # Only keep `entrez_gene_ids` that pass `min_genes`
-        metric.entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, min_genes) if keep]
-        metric.gene_sizes = [gene for gene, keep in zip(gene_size, min_genes) if keep]
+        metric.entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, min_genes, strict=True) if keep]
+        metric.gene_sizes = [gene for gene, keep in zip(gene_size, min_genes, strict=True) if keep]
         metric.count_matrix = metric.count_matrix.iloc[min_genes, :]
         metric.normalization_matrix = metrics[sample].normalization_matrix.iloc[min_genes, :]
 
-        keep_top_genes = [gene for gene, keep in zip(entrez_ids, top_genes) if keep]
-        metric.high_confidence_entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, keep_top_genes) if keep]
+        keep_top_genes = [gene for gene, keep in zip(entrez_ids, top_genes, strict=True) if keep]
+        metric.high_confidence_entrez_gene_ids = [
+            gene for gene, keep in zip(entrez_ids, keep_top_genes, strict=True) if keep
+        ]
 
     metrics = calculate_z_score(metrics)
 
@@ -583,13 +581,15 @@ def zfpkm_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions,
         min_samples = round(min_sample_expression * len(zfpkm_df.columns))
         min_func = k_over_a(min_samples, cut_off)
         min_genes: npt.NDArray[bool] = genefilter(zfpkm_df, min_func)
-        metric.entrez_gene_ids = [gene for gene, keep in zip(metric.entrez_gene_ids, min_genes) if keep]
+        metric.entrez_gene_ids = [gene for gene, keep in zip(metric.entrez_gene_ids, min_genes, strict=True) if keep]
 
         # determine which genes are confidently expressed
         top_samples = round(high_confidence_sample_expression * len(zfpkm_df.columns))
         top_func = k_over_a(top_samples, cut_off)
         top_genes: npt.NDArray[bool] = genefilter(zfpkm_df, top_func)
-        metric.high_confidence_entrez_gene_ids = [gene for gene, keep in zip(metric.entrez_gene_ids, top_genes) if keep]
+        metric.high_confidence_entrez_gene_ids = [
+            gene for gene, keep in zip(metric.entrez_gene_ids, top_genes, strict=True) if keep
+        ]
 
     return metrics
 
@@ -697,8 +697,7 @@ async def _save_rnaseq_tests(
 async def _create_metadata_df(path: Path) -> pd.DataFrame:
     if path.suffix not in {".xls", ".xlsx"}:
         raise ValueError(
-            f"Expected an excel file with extension of '.xlsx' or '.xls', got '{path.suffix}'. "
-            f"Attempted to process: {path}"
+            f"Expected an excel file with extension of '.xlsx' or '.xls', got '{path.suffix}'. Attempted to process: {path}"
         )
     return pd.read_excel(path)
 
diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 83eb504f..4f53962c 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -137,7 +137,7 @@ def _organize_gene_counts_files(data_dir: Path) -> list[_StudyMetrics]:
 
     # For each study, collect gene count files, fragment files, insert size files, layouts, and strandedness information
     study_metrics: list[_StudyMetrics] = []
-    for gene_dir, strand_dir in zip(gene_counts_directories, strandedness_directories):
+    for gene_dir, strand_dir in zip(gene_counts_directories, strandedness_directories, strict=True):
         count_files = list(gene_dir.glob("*.tab"))
         strand_files = list(strand_dir.glob("*.txt"))
         if len(count_files) == 0:
@@ -358,8 +358,7 @@ async def _create_config_df(  # noqa: C901
                 layout = file.read().strip()
         elif len(layout_files) > 1:
             raise ValueError(
-                f"Multiple matching layout files for {label}, "
-                f"make sure there is only one copy for each replicate in COMO_input"
+                f"Multiple matching layout files for {label}, make sure there is only one copy for each replicate in COMO_input"
             )
 
         strand = "UNKNOWN"
@@ -374,8 +373,7 @@ async def _create_config_df(  # noqa: C901
                 strand = file.read().strip()
         elif len(strand_files) > 1:
             raise ValueError(
-                f"Multiple matching strandedness files for {label}, "
-                f"make sure there is only one copy for each replicate in COMO_input"
+                f"Multiple matching strandedness files for {label}, make sure there is only one copy for each replicate in COMO_input"
             )
 
         prep = "total"
@@ -388,15 +386,13 @@ async def _create_config_df(  # noqa: C901
                     raise ValueError(f"Prep method must be either 'total' or 'mrna' for {label}")
         elif len(prep_files) > 1:
             raise ValueError(
-                f"Multiple matching prep files for {label}, "
-                f"make sure there is only one copy for each replicate in COMO_input"
+                f"Multiple matching prep files for {label}, make sure there is only one copy for each replicate in COMO_input"
             )
 
         mean_fragment_size = 100
         if len(frag_files) == 0 and prep != RNAPrepMethod.TOTAL.value:
             logger.warning(
-                f"No fragment file found for {label}, using '100'. "
-                "You should define this if you are going to use downstream zFPKM normalization"
+                f"No fragment file found for {label}, using '100'. You should define this if you are going to use downstream zFPKM normalization"
             )
         elif len(frag_files) == 1:
             if layout == "single-end":
@@ -420,8 +416,7 @@ async def _create_config_df(  # noqa: C901
                     mean_fragment_size = sum(mean_fragment_sizes * library_sizes) / sum(library_sizes)
         elif len(frag_files) > 1:
             raise ValueError(
-                f"Multiple matching fragment files for {label}, "
-                f"make sure there is only one copy for each replicate in COMO_input"
+                f"Multiple matching fragment files for {label}, make sure there is only one copy for each replicate in COMO_input"
             )
 
         sample_names.append(f"{context_name}_{study_number}{rep_number}")
diff --git a/pyproject.toml b/pyproject.toml
index aa9fff60..624ea4d7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,30 +19,27 @@ dependencies = [
     "plotly>=5.24.1",
     "pydantic-settings",
     "pydantic>=2.10.3",
-    "scanpy>=1.9.8",
+    "scanpy>=1.11.4",
     "scikit-learn>=1.5.2",
     "scipy>=1.7.3",
     "setuptools<60.0",
+    "statsmodels>=0.14",
     "troppo@git+https://github.com/JoshLoecker/troppo@master",
 ]
 
 [project.optional-dependencies]
-gurobi = [ "gurobipy>=10.0.0" ]
-interactive =   [
-    "ipython>=8.0.0",
-    "jupyterlab>=4.3.2"
+gurobi = ["gurobipy>=10.0.0"]
+interactive = [
+    "ipython>=7.0.0",
+    "jupyterlab>=4.0.0",
 ]
 dev = [
-    "commitlint>=1.3.0",
     "pytest-asyncio>=0.24.0",
-    "pytest>=8.3.3",
-    "ruff>=0.8.0",
-    "hypothesis>=6.122.1",
     "pytest-cov>=6.0.0",
-    "commitizen>=4.1.0",
+    "pytest>=8.0.0",
+    "ruff>=0.8.0",
 ]
 
-
 [tool.hatch.version]
 path = "main/como/__init__.py"
 
@@ -53,11 +50,4 @@ packages = ["main/como"]
 allow-direct-references = true
 
 [tool.pytest.ini_options]
-pythonpath = [ "main/src" ]
-
-[tool.commitizen]
-name = "cz_conventional_commits"
-tag_format = "$version"
-version_scheme = "semver2"
-version_provider = "pep621"
-update_changelog_on_bump = true
\ No newline at end of file
+pythonpath = ["main/src"]
diff --git a/uv.lock b/uv.lock
index 3a417c7a..0dcdbd6e 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,141 +1,166 @@
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+revision = 3
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 resolution-markers = [
-    "python_full_version < '3.11'",
-    "python_full_version == '3.11.*'",
     "python_full_version >= '3.12'",
+    "python_full_version == '3.11.*'",
+    "python_full_version < '3.11'",
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@@ -437,7 +432,6 @@ dependencies = [
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@@ -447,145 +441,196 @@ dependencies = [
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-    { name = "scipy", version = "1.14.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
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+    { name = "statsmodels" },
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     { name = "pytest-cov" },
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-    { name = "scanpy", specifier = ">=1.9.8" },
+    { name = "pydantic-settings" },
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+    { name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.24.0" },
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From 39e10f1b5bbf6925f5fc0060e78a68f8cd2fc864 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Thu, 2 Oct 2025 11:51:00 -0500
Subject: [PATCH 329/345] hotfix: log full name of directory if not found
 (#225)

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 main/COMO.ipynb                               | 54 +++++++--------
 main/como/cluster_rnaseq.py                   | 12 +---
 main/como/combine_distributions.py            | 54 +++------------
 main/como/create_context_specific_model.py    | 44 ++++--------
 main/como/knock_out_simulation.py             |  3 +-
 main/como/merge_xomics.py                     | 41 +++--------
 main/como/proteomics/Crux.py                  | 16 ++---
 main/como/proteomics/FTPManager.py            |  4 +-
 main/como/proteomics/FileInformation.py       |  4 +-
 main/como/proteomics/proteomics_preprocess.py | 17 ++---
 main/como/proteomics_gen.py                   |  5 +-
 main/como/proteomics_preprocessing.py         | 20 ++----
 main/como/rnaseq_gen.py                       | 69 +++++--------------
 main/como/rnaseq_preprocess.py                | 62 ++++-------------
 main/como/utils.py                            | 22 ++----
 ruff.toml                                     | 68 +++++++++---------
 tests/unit/test_rnaseq_preprocess.py          |  2 +-
 17 files changed, 153 insertions(+), 344 deletions(-)

diff --git a/main/COMO.ipynb b/main/COMO.ipynb
index 6b5b7f51..5bc09cb6 100644
--- a/main/COMO.ipynb
+++ b/main/COMO.ipynb
@@ -53,7 +53,7 @@
     "    3. Compare results between perturbed and unperturbed models (i.e., knocked-out models vs non-knocked-out models)\n",
     "    4. Integrate with disease genes and create a score of drug targets"
    ],
-   "id": "ea1c9614518c30bc"
+   "id": "90fba8b56a999517"
   },
   {
    "metadata": {},
@@ -223,7 +223,7 @@
     "    </tbody>\n",
     "</table>"
    ],
-   "id": "28d59417491e63db"
+   "id": "dcc8d38caab63fab"
   },
   {
    "metadata": {},
@@ -254,7 +254,7 @@
     "scrna_matrix_filepath = {context: Path(f\"data/results/{context}/scrna/scrna_{context}.csv\") for context in context_names}\n",
     "# scrna_matrix_filepath = [Path(f\"data/results/{context}/scrna/scrna_{context}.h5ad\") for context in context_names]\n"
    ],
-   "id": "18061578964ca096"
+   "id": "fd4c387eab48e9ee"
   },
   {
    "metadata": {},
@@ -266,7 +266,7 @@
     "- `taxon_id`: The [NCBI Taxon ID](https://www.ncbi.nlm.nih.gov/taxonomy) to use\n",
     "- `preprocess_mode`: This should be set to `\"create-matrix\"` if you are **not** providing a matrix, otherwise set it to `\"provide-matrix\"`"
    ],
-   "id": "b475e41b000d0cb5"
+   "id": "24a8475316ff6d70"
   },
   {
    "metadata": {},
@@ -291,7 +291,7 @@
     "        log_level=\"INFO\",\n",
     "    )"
    ],
-   "id": "dbfb32f2a0f7b8ef"
+   "id": "cb04960f17cc9b75"
   },
   {
    "metadata": {},
@@ -324,7 +324,7 @@
     "\n",
     "This method is not recommended, as zFPKM is much more robust for a similar level of \"hands-off\" model building\n"
    ],
-   "id": "a820a3985a2601c7"
+   "id": "4bd864328c93b27a"
   },
   {
    "metadata": {},
@@ -345,7 +345,7 @@
     "#### Single Cell RNA Sequencing\n",
     "While the Snakemake pipeline does not yet support single-cell alignment, and COMO does not yet support automated configuration file and counts matrix file creation for single-cell alignment output from STAR, it is possible to use single-cell data to create a model with COMO. Because normalization strategies can be applied to single-cell data in the same way it is applied to bulk RNA sequencing, `como/rnaseq_gen.py` can be used with a provided counts matrix and configuration file, from [Step 1](Step-1:-Initialize-and-Preprocess-RNA-seq-data), above. Just like `\"total\"` and `\"mRNA\"`, `como/rnaseq_gen.py` can be executed with `\"SC\"` as the \"`--library-prep`\" argument to help COMO differentiate it from any bulk RNA sequencing data if multiple strategies are being used."
    ],
-   "id": "a1d6608d045c7399"
+   "id": "e99e612919298aa8"
   },
   {
    "metadata": {},
@@ -364,7 +364,7 @@
     "- `min_zfpkm`: The cutoff for Counts-Per-Million filtering\n",
     "- `prep_method`: The library method used for preparation. Options are: `\"total\"`, `\"mRNA\"`, or `\"SC\"`,\n"
    ],
-   "id": "1fcf38e9cbc3de48"
+   "id": "8e550961a4a443b3"
   },
   {
    "metadata": {},
@@ -400,7 +400,7 @@
     "        cutoff=cutoff\n",
     "    )"
    ],
-   "id": "5a2e17b646d6de36"
+   "id": "d98c023e7738eb64"
   },
   {
    "metadata": {},
@@ -420,7 +420,7 @@
     "- `min_zfpkm`: The cutoff for Counts-Per-Million filtering\n",
     "- `prep_method`: The library method used for preparation. Options are: `\"total\"`, `\"mRNA\"`, or `\"SC\"`,\n"
    ],
-   "id": "87b5397301541650"
+   "id": "28848c3d315fa78c"
   },
   {
    "metadata": {},
@@ -456,7 +456,7 @@
     "        cutoff=cutoff\n",
     "    )"
    ],
-   "id": "3e73c07fa4a9e49a"
+   "id": "3e87f9890bc76e43"
   },
   {
    "metadata": {},
@@ -476,7 +476,7 @@
     "- `min_zfpkm`: The cutoff for Counts-Per-Million filtering\n",
     "- `prep_method`: The library method used for preparation. Options are: `\"total\"`, `\"mRNA\"`, or `\"scrna\"`,\n"
    ],
-   "id": "ae1278480199f415"
+   "id": "bfa9e2cd4152f961"
   },
   {
    "metadata": {},
@@ -512,7 +512,7 @@
     "        cutoff=cutoff\n",
     "    )"
    ],
-   "id": "567734d827571d6f"
+   "id": "c45085c6c12d9a3c"
   },
   {
    "metadata": {},
@@ -529,7 +529,7 @@
     "- `high_batch_ratio`: The ratio required before a gene is considered \"high-confidence\" in the study\n",
     "- `quantile`: The cutoff Transcripts-Per-Million quantile for filtering"
    ],
-   "id": "ea7004d23807dd1"
+   "id": "f01f21670b1e6956"
   },
   {
    "metadata": {},
@@ -556,7 +556,7 @@
     "        quantile=25,\n",
     "    )"
    ],
-   "id": "a7715d2cf9b0191a"
+   "id": "dcbae45189dd2eba"
   },
   {
    "metadata": {},
@@ -582,7 +582,7 @@
     "- `n_neighbors_context`: N nearest neighbors for context clustering. The default is `\"default\"`, which is the total number of contexts\n",
     "- `seed`: The random seed for clustering algorithm initialization. If not specified, `np.random.randint(0, 100000)` is used"
    ],
-   "id": "f4f395e25eeb1185"
+   "id": "354abde9f52ff9cf"
   },
   {
    "metadata": {},
@@ -626,7 +626,7 @@
     "\n",
     "!{cmd}"
    ],
-   "id": "cc61326b8df01fc4"
+   "id": "be527c98a8523323"
   },
   {
    "metadata": {},
@@ -666,7 +666,7 @@
     "\n",
     "Each of the \"weights\" (`total_rna_weight`, `mrna_weight`, etc.) are used to place a significance on each method. Becuase there are many steps in the Dogma from transcription to translation, the gene expression as seen by total RNA or mRNA sequencing may not be representative of the gene's protein expression, and this its metabolic impact. Because of this, you are able to weight each source more (or less) than another."
    ],
-   "id": "236fb36fa41f60b"
+   "id": "db0cfbec103d7d35"
   },
   {
    "metadata": {},
@@ -703,7 +703,7 @@
     "\n",
     "!{cmd}"
    ],
-   "id": "3ce78fc277c55885"
+   "id": "75b84df4d88d7cbe"
   },
   {
    "metadata": {},
@@ -770,7 +770,7 @@
     "- `force_reactions_filename`: The filename of the force reactions to be used. Force reactions will (as the name implies) force the optimizer to use these reactions, **no matter their expression**\n",
     "- `exclude_reactions_filename`: The filename of reactions to exclude from the model, no matter their expression"
    ],
-   "id": "b3a182617ff3eb7a"
+   "id": "30da70d405a80d23"
   },
   {
    "metadata": {},
@@ -839,7 +839,7 @@
     "        # fmt: on\n",
     "        !{cmd}"
    ],
-   "id": "e94c36e65ba46e38"
+   "id": "19918f482629d4fe"
   },
   {
    "metadata": {},
@@ -864,7 +864,7 @@
     "- `exampleTissue`: This is the name of the tissue context\n",
     "- `ALGORITHM`: This is the algorithm (`recon_algorithm`) used in the above model creation step\n"
    ],
-   "id": "47e3e576b43a040d"
+   "id": "38640adc60fe1909"
   },
   {
    "metadata": {},
@@ -941,7 +941,7 @@
     "\n",
     "        !{cmd}"
    ],
-   "id": "4a064f3322a3991e"
+   "id": "ef639305627a4a4"
   },
   {
    "metadata": {},
@@ -960,7 +960,7 @@
     "- `data_source`: The datasource you are using for disease analysis. This should be`\"rnaseq\"`\n",
     "- `taxon_id`: The [NCBI Taxon ID](https://www.ncbi.nlm.nih.gov/taxonomy) to use for disease analysis"
    ],
-   "id": "3aa49b19ab4fc4b9"
+   "id": "ad06b9e81d3d5f39"
   },
   {
    "metadata": {},
@@ -992,7 +992,7 @@
     "\n",
     "    !{cmd}"
    ],
-   "id": "7f946d38cac3a18c"
+   "id": "c9d0e6dfd35adefb"
   },
   {
    "metadata": {},
@@ -1027,7 +1027,7 @@
     "\n",
     "- `solver`: The solver you would like to use. Available options are `\"gurobi\"` or `\"glpk\"`\n"
    ],
-   "id": "feb9da60acb9a3b3"
+   "id": "7093fbd5a6551c18"
   },
   {
    "metadata": {},
@@ -1099,7 +1099,7 @@
     "            cmd = \" \".join(cmd)\n",
     "            !{cmd}"
    ],
-   "id": "c999b3f953381f9f"
+   "id": "281a24eba5e06fc9"
   }
  ],
  "metadata": {},
diff --git a/main/como/cluster_rnaseq.py b/main/como/cluster_rnaseq.py
index dba5499c..fc3a9f48 100644
--- a/main/como/cluster_rnaseq.py
+++ b/main/como/cluster_rnaseq.py
@@ -57,19 +57,13 @@ def __post_init__(self):  # noqa: C901, ignore too complex
         if 0 > self.min_distance > 1.0:
             raise ValueError("--min_dist must be a float between 0 and 1")
 
-        if (
-            isdigit(self.num_replicate_neighbors) and self.num_replicate_neighbors < 1
-        ) or self.num_replicate_neighbors != "default":
+        if (isdigit(self.num_replicate_neighbors) and self.num_replicate_neighbors < 1) or self.num_replicate_neighbors != "default":
             raise ValueError("--n-neighbors-rep must be either 'default' or an integer > 1")
 
-        if (
-            isdigit(self.num_batch_neighbors) and self.num_batch_neighbors < 1
-        ) or self.num_batch_neighbors != "default":
+        if (isdigit(self.num_batch_neighbors) and self.num_batch_neighbors < 1) or self.num_batch_neighbors != "default":
             raise ValueError("--n-neighbors-batch must be either 'default' or an integer > 1")
 
-        if (
-            isdigit(self.num_context_neighbors) and self.num_context_neighbors < 1
-        ) or self.num_context_neighbors != "default":
+        if (isdigit(self.num_context_neighbors) and self.num_context_neighbors < 1) or self.num_context_neighbors != "default":
             raise ValueError("--n-neighbors-context must be either 'default' or an integer > 1")
 
 
diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index ed69413b..68939dda 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -59,12 +59,7 @@ def _merge_batch(wd, context, batch):
         zmat.columns = rep_names
 
         stack_df = pd.concat(
-            [
-                pd.DataFrame(
-                    {"entrez_gene_id": zmat["entrez_gene_id"], "zscore": zmat[col].astype(float), "source": col}
-                )
-                for col in zmat.columns[1:]
-            ]
+            [pd.DataFrame({"entrez_gene_id": zmat["entrez_gene_id"], "zscore": zmat[col].astype(float), "source": col}) for col in zmat.columns[1:]]
         )
 
         plot_name_png = wd / "figures" / f"plot_{context}_{Path(f).stem}.png"
@@ -108,9 +103,7 @@ def weighted_z(x):
 
         stack_df = pd.concat(
             [
-                pd.DataFrame(
-                    {"entrez_gene_id": merge_df["entrez_gene_id"], "zscore": merge_df[col].astype(float), "source": col}
-                )
+                pd.DataFrame({"entrez_gene_id": merge_df["entrez_gene_id"], "zscore": merge_df[col].astype(float), "source": col})
                 for col in merge_df.columns[1:]
             ]
         )
@@ -157,9 +150,7 @@ def weighted_z(x, n_reps):
 
         stack_df = pd.concat(
             [
-                pd.DataFrame(
-                    {"entrez_gene_id": merge_df["entrez_gene_id"], "zscore": merge_df[col].astype(float), "source": col}
-                )
+                pd.DataFrame({"entrez_gene_id": merge_df["entrez_gene_id"], "zscore": merge_df[col].astype(float), "source": col})
                 for col in merge_df.columns[1:]
             ]
         )
@@ -259,9 +250,7 @@ def weighted_z(x, weights):
 
     stack_df = pd.concat(
         [
-            pd.DataFrame(
-                {"entrez_gene_id": merge_df["entrez_gene_id"], "zscore": merge_df[col].astype(float), "source": col}
-            )
+            pd.DataFrame({"entrez_gene_id": merge_df["entrez_gene_id"], "zscore": merge_df[col].astype(float), "source": col})
             for col in merge_df.columns[1:]
         ]
     )
@@ -343,11 +332,7 @@ def _combine_zscores(  # noqa: C901
                 num_reps.extend(res[1])
                 combine_z_matrix = _combine_batch_zdistro(trna_workdir, context, batch, z_matrix)
                 combine_z_matrix.columns = ["entrez_gene_id", batch]
-                merge_z_data = (
-                    combine_z_matrix
-                    if merge_z_data.empty
-                    else pd.merge(merge_z_data, combine_z_matrix, on="entrez_gene_id", how="outer")
-                )
+                merge_z_data = combine_z_matrix if merge_z_data.empty else pd.merge(merge_z_data, combine_z_matrix, on="entrez_gene_id", how="outer")
 
             comb_batches_z_trna = _combine_context_zdistro(trna_workdir, context, num_reps, merge_z_data)
             filename = trna_workdir / f"combined_zFPKM_{context}.csv"
@@ -369,11 +354,7 @@ def _combine_zscores(  # noqa: C901
                 num_reps.extend(res[1])
                 combine_z_matrix = _combine_batch_zdistro(mrna_workdir, context, batch, z_matrix)
                 combine_z_matrix.columns = ["entrez_gene_id", batch]
-                merge_z_data = (
-                    combine_z_matrix
-                    if merge_z_data.empty
-                    else pd.merge(merge_z_data, combine_z_matrix, on="entrez_gene_id", how="outer")
-                )
+                merge_z_data = combine_z_matrix if merge_z_data.empty else pd.merge(merge_z_data, combine_z_matrix, on="entrez_gene_id", how="outer")
 
             comb_batches_z_mrna = _combine_context_zdistro(mrna_workdir, context, num_reps, merge_z_data)
             filename = mrna_workdir / f"combined_zFPKM_{context}.csv"
@@ -395,11 +376,7 @@ def _combine_zscores(  # noqa: C901
                 num_reps.extend(res[1])
                 combine_z_matrix = _combine_batch_zdistro(scrna_workdir, context, batch, z_matrix)
                 combine_z_matrix.columns = ["entrez_gene_id", batch]
-                merge_z_data = (
-                    combine_z_matrix
-                    if merge_z_data.empty
-                    else pd.merge(merge_z_data, combine_z_matrix, on="entrez_gene_id", how="outer")
-                )
+                merge_z_data = combine_z_matrix if merge_z_data.empty else pd.merge(merge_z_data, combine_z_matrix, on="entrez_gene_id", how="outer")
 
             comb_batches_z_scrna = _combine_context_zdistro(scrna_workdir, context, num_reps, merge_z_data)
             filename = scrna_workdir / f"combined_zFPKM_{context}.csv"
@@ -421,11 +398,7 @@ def _combine_zscores(  # noqa: C901
                 num_reps.extend(res[1])
                 combine_z_matrix = _combine_batch_zdistro(protein_workdir, context, batch, z_matrix)
                 combine_z_matrix.columns = ["entrez_gene_id", batch]
-                merge_z_data = (
-                    combine_z_matrix
-                    if merge_z_data.empty
-                    else pd.merge(merge_z_data, combine_z_matrix, on="entrez_gene_id", how="outer")
-                )
+                merge_z_data = combine_z_matrix if merge_z_data.empty else pd.merge(merge_z_data, combine_z_matrix, on="entrez_gene_id", how="outer")
 
             comb_batches_z_prot = _combine_context_zdistro(protein_workdir, context, num_reps, merge_z_data)
             filename = protein_workdir / f"combined_zscore_proteinAbundance_{context}.csv"
@@ -435,15 +408,8 @@ def _combine_zscores(  # noqa: C901
                 filename = protein_workdir / f"model_scores_{context}.csv"
                 comb_batches_z_prot.to_csv(filename, index=False)
 
-        if (
-            comb_batches_z_mrna is None
-            and comb_batches_z_trna is None
-            and comb_batches_z_scrna is None
-            and comb_batches_z_prot is None
-        ):
-            logger.critical(
-                f"The context '{context}' was found in the configuration file but no data was found on disk!"
-            )
+        if comb_batches_z_mrna is None and comb_batches_z_trna is None and comb_batches_z_scrna is None and comb_batches_z_prot is None:
+            logger.critical(f"The context '{context}' was found in the configuration file but no data was found on disk!")
             continue
 
         comb_omics_z = _combine_omics_zdistros(
diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 4e483985..f6f71fa5 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -86,15 +86,9 @@ class _Arguments:
     def __post_init__(self):
         self.reference_model = Path(self.reference_model)
         self.active_genes_filepath = Path(self.active_genes_filepath)
-        self.boundary_reactions_filepath = (
-            Path(self.boundary_reactions_filepath) if self.boundary_reactions_filepath is not None else None
-        )
-        self.exclude_reactions_filepath = (
-            Path(self.exclude_reactions_filepath) if self.exclude_reactions_filepath is not None else None
-        )
-        self.force_reactions_filepath = (
-            Path(self.force_reactions_filepath) if self.force_reactions_filepath is not None else None
-        )
+        self.boundary_reactions_filepath = Path(self.boundary_reactions_filepath) if self.boundary_reactions_filepath is not None else None
+        self.exclude_reactions_filepath = Path(self.exclude_reactions_filepath) if self.exclude_reactions_filepath is not None else None
+        self.force_reactions_filepath = Path(self.force_reactions_filepath) if self.force_reactions_filepath is not None else None
 
         if not self.reference_model.exists():
             raise FileNotFoundError(f"Reference model not found at {self.reference_model}")
@@ -109,8 +103,7 @@ def __post_init__(self):
 
         if self.high_threshold < self.low_threshold:
             raise ValueError(
-                f"Low threshold must be less than high threshold. "
-                f"Received low threshold: {self.low_threshold}, high threshold: {self.high_threshold}"
+                f"Low threshold must be less than high threshold. Received low threshold: {self.low_threshold}, high threshold: {self.high_threshold}"
             )
 
 
@@ -283,12 +276,9 @@ def _build_with_fastcore(cobra_model, s_matrix, lb, ub, exp_idx_list, solver):
     # 'Vlassis, Pacheco, Sauter (2014). Fast reconstruction of compact
     # context-specific metabolic network models. PLoS Comput. Biol. 10,
     # e1003424.'
-    logger.warning(
-        "Fastcore requires a flux consistant model is used as refererence, "
-        "to achieve this fastcc is required which is NOT reproducible."
-    )
+    logger.warning("Fastcore requires a flux consistant model is used as refererence, to achieve this fastcc is required which is NOT reproducible.")
     logger.debug("Creating feasible model")
-    incon_rxns, cobra_model = _feasibility_test(cobra_model, "other")
+    _, cobra_model = _feasibility_test(cobra_model, "other")
     properties = FastcoreProperties(core=exp_idx_list, solver=solver)
     algorithm = FASTcore(s_matrix, lb, ub, properties)
     context_rxns = algorithm.fastcore()
@@ -383,10 +373,7 @@ def _map_expression_to_reaction(
         if gene_reaction_rule.strip() == "":
             continue
         for gid in gene_ids:
-            if gid in gene_expressions.index:
-                rep_val = f" {gene_expressions.at[gid, 'active']} "
-            else:
-                rep_val = f" {unknown_val!s} "
+            rep_val = f" {gene_expressions.at[gid, 'active']} " if gid in gene_expressions.index else f" {unknown_val!s} "
             gene_reaction_rule = f" {gene_reaction_rule} "  # pad white space to prevent gene matches inside floats
             gene_reaction_rule = gene_reaction_rule.replace(f" {gid} ", rep_val, 1)
         try:
@@ -435,9 +422,7 @@ def _build_model(  # noqa: C901
         case ".json":
             reference_model = cobra.io.load_json_model(general_model_file)
         case _:
-            raise NameError(
-                f"Reference reference_model format must be .xml, .mat, or .json, found '{general_model_file.suffix}'"
-            )
+            raise NameError(f"Reference reference_model format must be .xml, .mat, or .json, found '{general_model_file.suffix}'")
 
     reference_model.objective = {getattr(reference_model.reactions, objective): 1}  # set objective
 
@@ -445,7 +430,7 @@ def _build_model(  # noqa: C901
         force_rxns.append(objective)
 
     # set boundaries
-    reference_model, bound_rm_rxns = _set_boundaries(reference_model, bound_rxns, bound_lb, bound_ub)
+    reference_model, _ = _set_boundaries(reference_model, bound_rxns, bound_lb, bound_ub)
 
     # set solver
     reference_model.solver = solver.lower()
@@ -681,14 +666,10 @@ def create_context_specific_model(  # noqa: C901
     )
 
     config = Config()
-    build_results.infeasible_reactions.to_csv(
-        config.result_dir / context_name / f"{context_name}_infeasible_rxns.csv", index=False
-    )
+    build_results.infeasible_reactions.to_csv(config.result_dir / context_name / f"{context_name}_infeasible_rxns.csv", index=False)
 
     if algorithm == Algorithm.FASTCORE:
-        pd.DataFrame(build_results.expression_index_list).to_csv(
-            config.result_dir / context_name / f"{context_name}_core_rxns.csv", index=False
-        )
+        pd.DataFrame(build_results.expression_index_list).to_csv(config.result_dir / context_name / f"{context_name}_core_rxns.csv", index=False)
 
     output_directory = config.result_dir / context_name
     _write_model_to_disk(
@@ -789,8 +770,7 @@ def _parse_args():
         type=str,
         default="GIMME",
         dest="recon_algorithm",
-        help="Algorithm used to seed context specific model to the Genome-scale model. "
-        "Can be either GIMME, FASTCORE, iMAT, or tINIT.",
+        help="Algorithm used to seed context specific model to the Genome-scale model. Can be either GIMME, FASTCORE, iMAT, or tINIT.",
     )
     parser.add_argument(
         "-lt",
diff --git a/main/como/knock_out_simulation.py b/main/como/knock_out_simulation.py
index fe96c784..936996c7 100644
--- a/main/como/knock_out_simulation.py
+++ b/main/como/knock_out_simulation.py
@@ -280,8 +280,7 @@ def main(argv):
     parser = argparse.ArgumentParser(
         prog="knock_out_simulation.py",
         description="This script is responsible for mapping drug targets in metabolic models, performing knock out simulations, and comparing simulation results with disease genes. It also identified drug targets and repurposable drugs.",
-        epilog="For additional help, please post questions/issues in the MADRID GitHub repo at "
-        "https://github.com/HelikarLab/COMO",
+        epilog="For additional help, please post questions/issues in the MADRID GitHub repo at https://github.com/HelikarLab/COMO",
     )
     parser.add_argument(
         "-m",
diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 77d2336a..e6c996e2 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -80,14 +80,10 @@ def __post_init__(self):
         if self.expression_requirement.isdigit():
             self.expression_requirement = int(self.expression_requirement)
             if self.expression_requirement < 1:
-                logger.warning(
-                    f"Expression requirement should be at least 1, setting to 1 now. Got {self.expression_requirement}"
-                )
+                logger.warning(f"Expression requirement should be at least 1, setting to 1 now. Got {self.expression_requirement}")
                 self.expression_requirement = 1
         elif self.expression_requirement != "default":
-            raise ValueError(
-                f"Expression requirement should be an integer or 'default', got {self.expression_requirement}"
-            )
+            raise ValueError(f"Expression requirement should be an integer or 'default', got {self.expression_requirement}")
 
         if self.adjustment_method.value.lower() not in ["progressive", "regressive", "flat", "custom"]:
             raise ValueError("Adjust method must be either 'progressive', 'regressive', 'flat', or 'custom'")
@@ -119,10 +115,7 @@ def load_dummy_dict():
         case RNAPrepMethod.SCRNA:
             filename = f"rnaseq_scrna_{context_name}.csv"
         case _:
-            raise ValueError(
-                f"Unsupported RNA-seq library type: {prep_method.value}. "
-                f"Must be an option defined in 'RNASeqPreparationMethod'."
-            )
+            raise ValueError(f"Unsupported RNA-seq library type: {prep_method.value}. Must be an option defined in 'RNASeqPreparationMethod'.")
 
     save_filepath = config.result_dir / context_name / prep_method.value / filename
     if save_filepath.exists():
@@ -150,9 +143,7 @@ def _merge_logical_table(df: pd.DataFrame):
     df.dropna(axis=0, subset=["entrez_gene_id"], inplace=True)
     df["entrez_gene_id"] = df["entrez_gene_id"].astype(str).str.replace(" /// ", "//").astype(str)
 
-    id_list: list[str] = df.loc[
-        ~df["entrez_gene_id"].str.contains("//"), "entrez_gene_id"
-    ].tolist()  # Collect "single" ids, like "123"
+    id_list: list[str] = df.loc[~df["entrez_gene_id"].str.contains("//"), "entrez_gene_id"].tolist()  # Collect "single" ids, like "123"
     multiple_entrez_ids: list[str] = df.loc[
         df["entrez_gene_id"].str.contains("//"), "entrez_gene_id"
     ].tolist()  # Collect "double" ids, like "123//456"
@@ -285,8 +276,7 @@ async def _get_transcriptmoic_details(merged_df: pd.DataFrame) -> pd.DataFrame:
     ]
 
     gene_details["gene_info_type"] = [
-        i.group(1) if isinstance(i, re.Match) else "None"
-        for i in gene_details["Gene Info"].apply(lambda x: re.search(r"\[Gene Type: (.*)\]", x))
+        i.group(1) if isinstance(i, re.Match) else "None" for i in gene_details["Gene Info"].apply(lambda x: re.search(r"\[Gene Type: (.*)\]", x))
     ]
     gene_details["ensembl_info_type"] = [
         i.group(1) if isinstance(i, re.Match) else "None"
@@ -406,9 +396,7 @@ async def _merge_xomics(
         merge_data = prote_data if merge_data is None else merge_data.join(prote_data, how="outer")
 
     if merge_data is None:
-        logger.critical(
-            f"No data is available for the '{context_name}' context. If this is intentional, ignore this error."
-        )
+        logger.critical(f"No data is available for the '{context_name}' context. If this is intentional, ignore this error.")
         return {}
     merge_data = _merge_logical_table(merge_data)
 
@@ -423,9 +411,7 @@ async def _merge_xomics(
         )
     else:  # subtract one from requirement per NA
         merge_data.loc[:, "Required"] = merge_data[expression_list].apply(
-            lambda x: expression_requirement - (num_sources - x.count())
-            if (expression_requirement - (num_sources - x.count()) > 0)
-            else 1,
+            lambda x: expression_requirement - (num_sources - x.count()) if (expression_requirement - (num_sources - x.count()) > 0) else 1,
             axis=1,
         )
 
@@ -528,9 +514,7 @@ async def _handle_context_batch(  # noqa: C901
             case AdjustmentMethod.FLAT:
                 adjusted_expression_requirement = expression_requirement
             case _:
-                adjusted_expression_requirement = int(
-                    custom_df.iloc[custom_df["context"] == context_name, "req"].iloc[0]
-                )
+                adjusted_expression_requirement = int(custom_df.iloc[custom_df["context"] == context_name, "req"].iloc[0])
 
         if adjusted_expression_requirement != expression_requirement:
             logger.debug(
@@ -715,8 +699,7 @@ def _parse_args() -> _Arguments:
         required=False,
         default=None,
         dest="expression_requirement",
-        help="Number of sources with active gene for it to be considered active "
-        "even if it is not a high confidence-gene",
+        help="Number of sources with active gene for it to be considered active even if it is not a high confidence-gene",
     )
 
     parser.add_argument(
@@ -735,8 +718,7 @@ def _parse_args() -> _Arguments:
         required="custom" in sys.argv,  # required if --requriement-adjust is "custom",
         dest="custom_expression_filename",
         default=None,
-        help="Name of .xlsx file where first column is context names and "
-        "second column is expression requirement for that context",
+        help="Name of .xlsx file where first column is context names and second column is expression requirement for that context",
     )
 
     parser.add_argument(
@@ -746,8 +728,7 @@ def _parse_args() -> _Arguments:
         required=False,
         default=False,
         dest="no_high_confidence",
-        help="Prevent high-confidence genes forcing a gene to be used in final model, "
-        "irrespective of other other data sources",
+        help="Prevent high-confidence genes forcing a gene to be used in final model, irrespective of other other data sources",
     )
 
     parser.add_argument(
diff --git a/main/como/proteomics/Crux.py b/main/como/proteomics/Crux.py
index b9d0107f..9351cf4e 100644
--- a/main/como/proteomics/Crux.py
+++ b/main/como/proteomics/Crux.py
@@ -128,9 +128,7 @@ def mzml_to_sqt(self) -> None:
             )
 
             # Replace all "comet.*" in output directory with the name of the file being processed
-            comet_files = [
-                str(file) for file in os.listdir(file_information.sqt_base_path) if str(file).startswith("comet.")
-            ]
+            comet_files = [str(file) for file in os.listdir(file_information.sqt_base_path) if str(file).startswith("comet.")]
             for file_name in comet_files:
                 # Determine the old file path
                 old_file_path: Path = Path(file_information.sqt_base_path, file_name)
@@ -242,9 +240,7 @@ def collect_uniprot_ids_and_ion_intensity(self) -> None:
 
             # Assign the file_information intensity dataframe to the gathered values
             self._file_information[i].intensity_df = pd.DataFrame(average_intensities_dict)
-            self._file_information[i].intensity_df = (
-                self._file_information[i].intensity_df.groupby("uniprot", as_index=False).mean()
-            )
+            self._file_information[i].intensity_df = self._file_information[i].intensity_df.groupby("uniprot", as_index=False).mean()
 
     async def _convert_uniprot_wrapper(self) -> None:
         """This function is a multiprocessing wrapper around the convert_ids function"""
@@ -252,9 +248,7 @@ async def _convert_uniprot_wrapper(self) -> None:
 
         # Create a progress bar of results
         # From: https://stackoverflow.com/a/61041328/
-        progress_bar = tqdm.tqdm(
-            desc="Starting UniProt to Gene Symbol conversion... ", total=len(self._file_information)
-        )
+        progress_bar = tqdm.tqdm(desc="Starting UniProt to Gene Symbol conversion... ", total=len(self._file_information))
         for i, result in enumerate(asyncio.as_completed(values)):
             await result  # Get result from asyncio.as_completed
             progress_bar.set_description(f"Working on {i + 1} of {len(self._file_information)}")
@@ -378,9 +372,7 @@ def split_abundance_values(self) -> None:
                 # Create a new dataframe to split the S# columns from
                 split_frame: pd.DataFrame = dataframe.copy()
                 # Get the current S{i} columns in
-                abundance_columns: list[str] = [
-                    column for column in split_frame.columns if re.match(rf"{cell_type}_S{i}R\d+", column)
-                ]
+                abundance_columns: list[str] = [column for column in split_frame.columns if re.match(rf"{cell_type}_S{i}R\d+", column)]
                 take_columns: list[str] = ["symbol"] + abundance_columns
                 average_intensity_name: str = f"{cell_type}_S{i}"
 
diff --git a/main/como/proteomics/FTPManager.py b/main/como/proteomics/FTPManager.py
index 1be45d91..f846481d 100644
--- a/main/como/proteomics/FTPManager.py
+++ b/main/como/proteomics/FTPManager.py
@@ -18,9 +18,7 @@
 from .FileInformation import FileInformation, clear_print
 
 
-async def aioftp_client(
-    host: str, username: str = "anonymous", password: str = "guest", port: int = 21, max_attempts: int = 3
-) -> aioftp.Client:
+async def aioftp_client(host: str, username: str = "anonymous", password: str = "guest", port: int = 21, max_attempts: int = 3) -> aioftp.Client:
     """This class is responsible for creating a "client" connection"""
     connection_successful: bool = False
     attempt_num: int = 1
diff --git a/main/como/proteomics/FileInformation.py b/main/como/proteomics/FileInformation.py
index 0b37a4b6..59a4a2ee 100644
--- a/main/como/proteomics/FileInformation.py
+++ b/main/como/proteomics/FileInformation.py
@@ -68,9 +68,7 @@ def __init__(
             self.sqt_base_path: Path = sqt_path.parent
 
         if intensity_csv is None:
-            self.intensity_csv: Path = Path(
-                project.configs.data_dir, "data_matrices", cell_type, f"protein_abundance_matrix_{cell_type}.csv"
-            )
+            self.intensity_csv: Path = Path(project.configs.data_dir, "data_matrices", cell_type, f"protein_abundance_matrix_{cell_type}.csv")
         else:
             self.intensity_csv: Path = intensity_csv
 
diff --git a/main/como/proteomics/proteomics_preprocess.py b/main/como/proteomics/proteomics_preprocess.py
index 85d78ec2..fe363655 100644
--- a/main/como/proteomics/proteomics_preprocess.py
+++ b/main/como/proteomics/proteomics_preprocess.py
@@ -159,9 +159,7 @@ def _gather_data(self):
 
             # Iterate through the URLs available
             for url, study in zip(ftp_urls, studies, strict=True):
-                ftp_data: FTPManager.Reader = FTPManager.Reader(
-                    root_link=url, file_extensions=self._preferred_extensions
-                )
+                ftp_data: FTPManager.Reader = FTPManager.Reader(root_link=url, file_extensions=self._preferred_extensions)
 
                 urls = list(ftp_data.files)
                 sizes = list(ftp_data.file_sizes)
@@ -169,9 +167,7 @@ def _gather_data(self):
 
                 # Iterate through all files and sizes found for url_##
                 for file, size in zip(urls, sizes, strict=True):
-                    self.file_information.append(
-                        FileInformation(cell_type=cell_type, download_url=file, file_size=size, study=study)
-                    )
+                    self.file_information.append(FileInformation(cell_type=cell_type, download_url=file, file_size=size, study=study))
 
     def print_download_size(self):
         """Print the total size to download if we must download data."""
@@ -313,16 +309,11 @@ def parse_args() -> argparse.Namespace:
     if args.core_count == "all":
         args.core_count = os.cpu_count()
     elif not str(args.core_count).isdigit():
-        raise ValueError(
-            f"Invalid option '{args.core_count}' for option '--cores'. Enter an integer or 'all' to use all cores"
-        )
+        raise ValueError(f"Invalid option '{args.core_count}' for option '--cores'. Enter an integer or 'all' to use all cores")
     else:
         args.core_count = int(args.core_count)
         if args.core_count > os.cpu_count():
-            logger.info(
-                f"{args.core_count} cores not available, system only has {os.cpu_count()} cores. "
-                f"Setting '--cores' to {os.cpu_count()}"
-            )
+            logger.info(f"{args.core_count} cores not available, system only has {os.cpu_count()} cores. Setting '--cores' to {os.cpu_count()}")
             args.core_count = os.cpu_count()
 
     return args
diff --git a/main/como/proteomics_gen.py b/main/como/proteomics_gen.py
index d020d71d..c205aea4 100644
--- a/main/como/proteomics_gen.py
+++ b/main/como/proteomics_gen.py
@@ -137,10 +137,7 @@ def load_empty_dict():
         return context_name, data
 
     else:
-        logger.warning(
-            f"Proteomics gene expression file for {context_name} was not found at {full_save_filepath}. "
-            f"Is this intentional?"
-        )
+        logger.warning(f"Proteomics gene expression file for {context_name} was not found at {full_save_filepath}. Is this intentional?")
         return load_empty_dict()
 
 
diff --git a/main/como/proteomics_preprocessing.py b/main/como/proteomics_preprocessing.py
index bced01b0..4093dba0 100644
--- a/main/como/proteomics_preprocessing.py
+++ b/main/como/proteomics_preprocessing.py
@@ -36,15 +36,9 @@ def z_score_calc(abundance: pd.DataFrame, min_thresh: int) -> ZResult:
 
     # np.zeros((1000, len(abundance.columns)), dtype=np.float64),
     z_result = ZResult(
-        zfpkm=pd.DataFrame(
-            data=np.nan * np.ones_like(values), index=abundance.index, columns=abundance.columns, dtype=np.float64
-        ),
-        x_range=pd.DataFrame(
-            data=np.zeros((1000, len(abundance.columns))), columns=abundance.columns, dtype=np.float64
-        ),
-        density=pd.DataFrame(
-            data=np.zeros((1000, len(abundance.columns))), columns=abundance.columns, dtype=np.float64
-        ),
+        zfpkm=pd.DataFrame(data=np.nan * np.ones_like(values), index=abundance.index, columns=abundance.columns, dtype=np.float64),
+        x_range=pd.DataFrame(data=np.zeros((1000, len(abundance.columns))), columns=abundance.columns, dtype=np.float64),
+        density=pd.DataFrame(data=np.zeros((1000, len(abundance.columns))), columns=abundance.columns, dtype=np.float64),
         mu=np.zeros(len(abundance.columns)),
         std_dev=np.zeros(len(abundance.columns)),
         max_fpkm_peak=np.zeros(len(abundance.columns)),
@@ -98,9 +92,7 @@ def plot_gaussian_fit(z_results: ZResult, facet_titles: bool = True, x_min: int
     std_dev = z_results.std_dev
     max_fpkm = z_results.max_fpkm_peak
 
-    fig = make_subplots(
-        rows=len(zfpkm.columns), cols=1, subplot_titles=zfpkm.columns if facet_titles else [None] * len(zfpkm.columns)
-    )
+    fig = make_subplots(rows=len(zfpkm.columns), cols=1, subplot_titles=zfpkm.columns if facet_titles else [None] * len(zfpkm.columns))
     for i, col in enumerate(zfpkm.columns):
         fitted = stats.norm.pdf(x_range[col], loc=mu[i], scale=std_dev[i])
         scale_fit = fitted * (max_fpkm[i] / fitted.max())
@@ -136,9 +128,7 @@ def protein_transform_main(
     output_z_score_matrix_filepath: Path,
 ) -> None:
     """Transform protein abundance data."""
-    abundance_df: pd.DataFrame = (
-        pd.read_csv(abundance_df) if isinstance(abundance_df, str | Path) else abundance_df.fillna(0)
-    )
+    abundance_df: pd.DataFrame = pd.read_csv(abundance_df) if isinstance(abundance_df, str | Path) else abundance_df.fillna(0)
     abundance_df = abundance_df[np.isfinite(abundance_df).all(axis=1)]  # Remove +/- infinity values
     z_transform: ZResult = z_score_calc(abundance_df, min_thresh=0)
 
diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index b0079b8c..53da3c69 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -3,7 +3,8 @@
 import math
 import multiprocessing
 import time
-from collections import Callable, namedtuple
+from collections import namedtuple
+from collections.abc import Callable
 from dataclasses import dataclass, field
 from enum import Enum
 from functools import partial
@@ -114,7 +115,7 @@ def k_over_a(k: int, a: float) -> Callable[[npt.NDArray], bool]:
     :param k: The minimum number of times the sum of elements must be greater than or equal to A.
     :param a: The value to compare the sum of elements to.
     :return: A function that accepts a NumPy array to perform the actual filtering
-    """  # noqa: E501
+    """
 
     def filter_func(row: npt.NDArray) -> bool:
         return np.sum(row >= a) >= k
@@ -134,11 +135,7 @@ def genefilter(data: pd.DataFrame | npt.NDArray, filter_func: Callable[[npt.NDAr
     if not isinstance(data, (pd.DataFrame, npt.NDArray)):
         raise TypeError("Unsupported data type. Must be a Pandas DataFrame or a NumPy array.")
 
-    return (
-        data.apply(filter_func, axis=1).values
-        if isinstance(data, pd.DataFrame)
-        else np.apply_along_axis(filter_func, axis=1, arr=data)
-    )
+    return data.apply(filter_func, axis=1).values if isinstance(data, pd.DataFrame) else np.apply_along_axis(filter_func, axis=1, arr=data)
 
 
 async def _read_counts(path: Path) -> pd.DataFrame:
@@ -236,7 +233,7 @@ def calculate_tpm(metrics: NamedMetrics) -> NamedMetrics:
 
 
 def calculate_fpkm(metrics: NamedMetrics) -> NamedMetrics:
-    """Calculate the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) for each sample in the metrics dictionary."""  # noqa: E501
+    """Calculate the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) for each sample in the metrics dictionary."""
     matrix_values = []
     for study in metrics:
         for sample in range(metrics[study].num_samples):
@@ -348,22 +345,18 @@ def zfpkm_transform(
 ) -> tuple[dict[str, _ZFPKMResult], DataFrame]:
     """Perform zFPKM calculation/transformation."""
     if update_every_percent > 1:
-        logger.warning(
-            f"update_every_percent should be a decimal value between 0 and 1; got: {update_every_percent} - will convert to percentage"
-        )
+        logger.warning(f"update_every_percent should be a decimal value between 0 and 1; got: {update_every_percent} - will convert to percentage")
         update_every_percent /= 100
 
     total = len(fpkm_df.columns)
     update_per_step: int = int(np.ceil(total * update_every_percent))
     cores = min(multiprocessing.cpu_count() - 2, total)
     logger.debug(f"Processing {total:,} samples through zFPKM transform using {cores} cores")
-    logger.debug(
-        f"Will update every {update_per_step:,} steps as this is approximately {update_every_percent:.1%} of {total:,}"
-    )
+    logger.debug(f"Will update every {update_per_step:,} steps as this is approximately {update_every_percent:.1%} of {total:,}")
 
     with Pool(processes=cores) as pool:
         kernel = KernelDensity(kernel="gaussian", bandwidth=bandwidth)
-        chunksize = int(math.ceil(len(fpkm_df.columns) / (4 * cores)))
+        chunksize = math.ceil(len(fpkm_df.columns) / (4 * cores))
         partial_func = partial(_zfpkm_calculation, kernel=kernel, peak_parameters=peak_parameters)
         chunk_time = time.time()
         start_time = time.time()
@@ -452,9 +445,7 @@ def calculate_z_score(metrics: NamedMetrics) -> NamedMetrics:
     """Calculate the z-score for each sample in the metrics dictionary."""
     for sample in metrics:
         log_matrix = np.log(metrics[sample].normalization_matrix)
-        z_matrix = pd.DataFrame(
-            data=sklearn.preprocessing.scale(log_matrix, axis=1), columns=metrics[sample].sample_names
-        )
+        z_matrix = pd.DataFrame(data=sklearn.preprocessing.scale(log_matrix, axis=1), columns=metrics[sample].sample_names)
         metrics[sample].z_score_matrix = z_matrix
     return metrics
 
@@ -498,11 +489,7 @@ def cpm_filter(
         top_samples = round(n_top * len(counts.columns))  # noqa: F841
         test_bools = pd.DataFrame({"entrez_gene_ids": entrez_ids})
         for i in range(len(counts_per_million.columns)):
-            cutoff = (
-                10e6 / (np.median(np.sum(counts[:, i])))
-                if cut_off == "default"
-                else (1e6 * cut_off) / np.median(np.sum(counts[:, i]))
-            )
+            cutoff = 10e6 / (np.median(np.sum(counts[:, i]))) if cut_off == "default" else (1e6 * cut_off) / np.median(np.sum(counts[:, i]))
             test_bools = test_bools.merge(counts_per_million[counts_per_million.iloc[:, i] > cutoff])
 
     return metrics
@@ -528,12 +515,8 @@ def tpm_quantile_filter(*, metrics: NamedMetrics, filtering_options: _FilteringO
         top_samples = round(n_top * len(tpm_matrix.columns))
 
         tpm_quantile = tpm_matrix[tpm_matrix > 0]
-        quantile_cutoff = np.quantile(
-            a=tpm_quantile.values, q=1 - (cut_off / 100), axis=0
-        )  # Compute quantile across columns
-        boolean_expression = pd.DataFrame(
-            data=tpm_matrix > quantile_cutoff, index=tpm_matrix.index, columns=tpm_matrix.columns
-        ).astype(int)
+        quantile_cutoff = np.quantile(a=tpm_quantile.values, q=1 - (cut_off / 100), axis=0)  # Compute quantile across columns
+        boolean_expression = pd.DataFrame(data=tpm_matrix > quantile_cutoff, index=tpm_matrix.index, columns=tpm_matrix.columns).astype(int)
 
         min_func = k_over_a(min_samples, 0.9)
         top_func = k_over_a(top_samples, 0.9)
@@ -548,9 +531,7 @@ def tpm_quantile_filter(*, metrics: NamedMetrics, filtering_options: _FilteringO
         metric.normalization_matrix = metrics[sample].normalization_matrix.iloc[min_genes, :]
 
         keep_top_genes = [gene for gene, keep in zip(entrez_ids, top_genes, strict=True) if keep]
-        metric.high_confidence_entrez_gene_ids = [
-            gene for gene, keep in zip(entrez_ids, keep_top_genes, strict=True) if keep
-        ]
+        metric.high_confidence_entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, keep_top_genes, strict=True) if keep]
 
     metrics = calculate_z_score(metrics)
 
@@ -587,9 +568,7 @@ def zfpkm_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions,
         top_samples = round(high_confidence_sample_expression * len(zfpkm_df.columns))
         top_func = k_over_a(top_samples, cut_off)
         top_genes: npt.NDArray[bool] = genefilter(zfpkm_df, top_func)
-        metric.high_confidence_entrez_gene_ids = [
-            gene for gene, keep in zip(metric.entrez_gene_ids, top_genes, strict=True) if keep
-        ]
+        metric.high_confidence_entrez_gene_ids = [gene for gene, keep in zip(metric.entrez_gene_ids, top_genes, strict=True) if keep]
 
     return metrics
 
@@ -605,9 +584,7 @@ def filter_counts(
     """Filter the count matrix based on the specified technique."""
     match technique:
         case FilteringTechnique.cpm:
-            return cpm_filter(
-                context_name=context_name, metrics=metrics, filtering_options=filtering_options, prep=prep
-            )
+            return cpm_filter(context_name=context_name, metrics=metrics, filtering_options=filtering_options, prep=prep)
         case FilteringTechnique.tpm:
             return tpm_quantile_filter(metrics=metrics, filtering_options=filtering_options)
         case FilteringTechnique.zfpkm:
@@ -666,9 +643,7 @@ async def _save_rnaseq_tests(
         top_genes.extend(metric.high_confidence_entrez_gene_ids)
 
     expression_frequency = pd.Series(expressed_genes).value_counts()
-    expression_df = pd.DataFrame(
-        {"entrez_gene_id": expression_frequency.index, "frequency": expression_frequency.values}
-    )
+    expression_df = pd.DataFrame({"entrez_gene_id": expression_frequency.index, "frequency": expression_frequency.values})
     expression_df["prop"] = expression_df["frequency"] / len(metrics)
     expression_df = expression_df[expression_df["prop"] >= filtering_options.batch_ratio]
 
@@ -688,17 +663,13 @@ async def _save_rnaseq_tests(
     high_confidence_count = len(boolean_matrix[boolean_matrix["high"] == 1])
 
     boolean_matrix.to_csv(output_filepath, index=False)
-    logger.info(
-        f"{context_name} - Found {expressed_count} expressed and {high_confidence_count} confidently expressed genes"
-    )
+    logger.info(f"{context_name} - Found {expressed_count} expressed and {high_confidence_count} confidently expressed genes")
     logger.success(f"Wrote boolean matrix to {output_filepath}")
 
 
 async def _create_metadata_df(path: Path) -> pd.DataFrame:
     if path.suffix not in {".xls", ".xlsx"}:
-        raise ValueError(
-            f"Expected an excel file with extension of '.xlsx' or '.xls', got '{path.suffix}'. Attempted to process: {path}"
-        )
+        raise ValueError(f"Expected an excel file with extension of '.xlsx' or '.xls', got '{path.suffix}'. Attempted to process: {path}")
     return pd.read_excel(path)
 
 
@@ -746,9 +717,7 @@ async def rnaseq_gen(  # noqa: C901, allow complex function
     if not input_metadata_df and not input_metadata_filepath:
         raise ValueError("At least one of input_metadata_filepath or input_metadata_df must be provided")
 
-    technique = (
-        FilteringTechnique.from_string(str(technique.lower())) if isinstance(technique, (str, int)) else technique
-    )
+    technique = FilteringTechnique.from_string(str(technique.lower())) if isinstance(technique, (str, int)) else technique
 
     match technique:
         case FilteringTechnique.tpm:
diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 4f53962c..dac056fe 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -131,7 +131,7 @@ def _organize_gene_counts_files(data_dir: Path) -> list[_StudyMetrics]:
     if len(gene_counts_directories) != len(strandedness_directories):
         raise ValueError(
             f"Unequal number of gene count directories and strandedness directories. "
-            f"Found {len(gene_counts_directories)} gene count directories and {len(strandedness_directories)} strandedness directories."  # noqa: E501
+            f"Found {len(gene_counts_directories)} gene count directories and {len(strandedness_directories)} strandedness directories."
             f"\nGene count directory: {gene_count_dir}\nStrandedness directory: {strand_dir}"
         )
 
@@ -172,11 +172,7 @@ async def _process_first_multirun_sample(strand_file: Path, all_counts_files: li
 
         run_counts = star_information.count_matrix[["ensembl_gene_id", strand_information]]
         run_counts.columns = pd.Index(["ensembl_gene_id", "counts"])
-        sample_count = (
-            run_counts
-            if sample_count.empty
-            else sample_count.merge(run_counts, on=["ensembl_gene_id", "counts"], how="outer")
-        )
+        sample_count = run_counts if sample_count.empty else sample_count.merge(run_counts, on=["ensembl_gene_id", "counts"], how="outer")
 
     # Set na values to 0
     sample_count = sample_count.fillna(value="0")
@@ -261,9 +257,7 @@ async def _write_counts_matrix(
 ) -> pd.DataFrame:
     """Create a counts matrix file by reading gene counts table(s)."""
     study_metrics = _organize_gene_counts_files(data_dir=como_context_dir)
-    counts: list[pd.DataFrame] = await asyncio.gather(
-        *[_create_sample_counts_matrix(metric) for metric in study_metrics]
-    )
+    counts: list[pd.DataFrame] = await asyncio.gather(*[_create_sample_counts_matrix(metric) for metric in study_metrics])
 
     final_matrix = pd.DataFrame()
     for count in counts:
@@ -357,9 +351,7 @@ async def _create_config_df(  # noqa: C901
             with layout_files[0].open("r") as file:
                 layout = file.read().strip()
         elif len(layout_files) > 1:
-            raise ValueError(
-                f"Multiple matching layout files for {label}, make sure there is only one copy for each replicate in COMO_input"
-            )
+            raise ValueError(f"Multiple matching layout files for {label}, make sure there is only one copy for each replicate in COMO_input")
 
         strand = "UNKNOWN"
         if len(strand_files) == 0:
@@ -372,9 +364,7 @@ async def _create_config_df(  # noqa: C901
             with strand_files[0].open("r") as file:
                 strand = file.read().strip()
         elif len(strand_files) > 1:
-            raise ValueError(
-                f"Multiple matching strandedness files for {label}, make sure there is only one copy for each replicate in COMO_input"
-            )
+            raise ValueError(f"Multiple matching strandedness files for {label}, make sure there is only one copy for each replicate in COMO_input")
 
         prep = "total"
         if len(prep_files) == 0:
@@ -385,9 +375,7 @@ async def _create_config_df(  # noqa: C901
                 if prep not in ["total", "mrna"]:
                     raise ValueError(f"Prep method must be either 'total' or 'mrna' for {label}")
         elif len(prep_files) > 1:
-            raise ValueError(
-                f"Multiple matching prep files for {label}, make sure there is only one copy for each replicate in COMO_input"
-            )
+            raise ValueError(f"Multiple matching prep files for {label}, make sure there is only one copy for each replicate in COMO_input")
 
         mean_fragment_size = 100
         if len(frag_files) == 0 and prep != RNAPrepMethod.TOTAL.value:
@@ -415,9 +403,7 @@ async def _create_config_df(  # noqa: C901
 
                     mean_fragment_size = sum(mean_fragment_sizes * library_sizes) / sum(library_sizes)
         elif len(frag_files) > 1:
-            raise ValueError(
-                f"Multiple matching fragment files for {label}, make sure there is only one copy for each replicate in COMO_input"
-            )
+            raise ValueError(f"Multiple matching fragment files for {label}, make sure there is only one copy for each replicate in COMO_input")
 
         sample_names.append(f"{context_name}_{study_number}{rep_number}")
         fragment_lengths.append(mean_fragment_size)
@@ -460,9 +446,7 @@ async def read_counts(file: Path) -> list[str]:
         )
         return conversion["entrez_gene_id"].tolist()
 
-    logger.info(
-        "Fetching gene info (this may take 1-5 minutes depending on the number of genes and your internet connection)"
-    )
+    logger.info("Fetching gene info (this may take 1-5 minutes depending on the number of genes and your internet connection)")
     genes = set(chain.from_iterable(await asyncio.gather(*[read_counts(f) for f in counts_matrix_filepaths])))
     gene_data = await MyGene(cache=cache).query(items=list(genes), taxon=taxon, scopes="entrezgene")
     gene_info: pd.DataFrame = pd.DataFrame(
@@ -486,13 +470,7 @@ async def read_counts(file: Path) -> list[str]:
         gene_info.at[i, "start_position"] = start_pos
         gene_info.at[i, "end_position"] = end_pos
 
-    gene_info = gene_info[
-        (
-            (gene_info["entrez_gene_id"] != "-")
-            & (gene_info["ensembl_gene_id"] != "-")
-            & (gene_info["gene_symbol"] != "-")
-        )
-    ]
+    gene_info = gene_info[((gene_info["entrez_gene_id"] != "-") & (gene_info["ensembl_gene_id"] != "-") & (gene_info["gene_symbol"] != "-"))]
     gene_info["size"] = gene_info["end_position"].astype(int) - gene_info["start_position"].astype(int)
     gene_info.drop(columns=["start_position", "end_position"], inplace=True)
     gene_info.sort_values(by="ensembl_gene_id", inplace=True)
@@ -556,11 +534,7 @@ async def _process(
 
     # create the gene info filepath based on provided data
     await _create_gene_info_file(
-        counts_matrix_filepaths=[
-            f
-            for f in [*input_matrix_filepath, output_trna_matrix_filepath, output_mrna_matrix_filepath]
-            if f is not None
-        ],
+        counts_matrix_filepaths=[f for f in [*input_matrix_filepath, output_trna_matrix_filepath, output_mrna_matrix_filepath] if f is not None],
         output_filepath=output_gene_info_filepath,
         taxon=taxon,
         cache=cache,
@@ -611,21 +585,13 @@ async def rnaseq_preprocess(
     output_gene_info_filepath = output_gene_info_filepath.resolve()
     como_context_dir = como_context_dir.resolve()
     input_matrix_filepath = [i.resolve() for i in _listify(input_matrix_filepath)] if input_matrix_filepath else None
-    output_trna_config_filepath = (
-        output_trna_config_filepath.resolve() if output_trna_config_filepath else output_trna_config_filepath
-    )
-    output_mrna_config_filepath = (
-        output_mrna_config_filepath.resolve() if output_mrna_config_filepath else output_mrna_config_filepath
-    )
+    output_trna_config_filepath = output_trna_config_filepath.resolve() if output_trna_config_filepath else output_trna_config_filepath
+    output_mrna_config_filepath = output_mrna_config_filepath.resolve() if output_mrna_config_filepath else output_mrna_config_filepath
     output_trna_count_matrix_filepath = (
-        output_trna_count_matrix_filepath.resolve()
-        if output_trna_count_matrix_filepath
-        else output_trna_count_matrix_filepath
+        output_trna_count_matrix_filepath.resolve() if output_trna_count_matrix_filepath else output_trna_count_matrix_filepath
     )
     output_mrna_count_matrix_filepath = (
-        output_mrna_count_matrix_filepath.resolve()
-        if output_mrna_count_matrix_filepath
-        else output_mrna_count_matrix_filepath
+        output_mrna_count_matrix_filepath.resolve() if output_mrna_count_matrix_filepath else output_mrna_count_matrix_filepath
     )
 
     input_matrix_filepath = _listify(input_matrix_filepath)
diff --git a/main/como/utils.py b/main/como/utils.py
index a359de0f..7e341d06 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -95,9 +95,7 @@ class Compartments:
         "s": ["eyespot", "eyespot apparatus", "stigma"],
     }
 
-    _REVERSE_LOOKUP: ClassVar[dict[str, list[str]]] = {
-        value.lower(): key for key, values in SHORTHAND.items() for value in values
-    }
+    _REVERSE_LOOKUP: ClassVar[dict[str, list[str]]] = {value.lower(): key for key, values in SHORTHAND.items() for value in values}
 
     @classmethod
     def get(cls, longhand: str) -> str | None:
@@ -125,20 +123,14 @@ def stringlist_to_list(stringlist: str | list[str]) -> list[str]:
     new_list: list[str] = stringlist.strip("[]").replace("'", "").replace(" ", "").split(",")
 
     # Show a warning if more than one item is present in the list (this means we are using the old method)
-    logger.critical(
-        "DeprecationWarning: Please use the new method of providing context names, "
-        "i.e. --output-filetypes 'type1 type2 type3'."
-    )
+    logger.critical("DeprecationWarning: Please use the new method of providing context names, i.e. --output-filetypes 'type1 type2 type3'.")
     logger.critical(
         "If you are using COMO, this can be done by setting the 'context_names' variable to a "
         "simple string separated by spaces. Here are a few examples!"
     )
     logger.critical("context_names = 'cellType1 cellType2 cellType3'")
     logger.critical("output_filetypes = 'output1 output2 output3'")
-    logger.critical(
-        "\nYour current method of passing context names will be removed in the future. "
-        "Update your variables above accordingly!\n\n"
-    )
+    logger.critical("\nYour current method of passing context names will be removed in the future. Update your variables above accordingly!\n\n")
 
     return new_list
 
@@ -158,9 +150,7 @@ def split_gene_expression_data(expression_data: pd.DataFrame, recon_algorithm: A
     expression_data.loc[:, "entrez_gene_id"] = expression_data["entrez_gene_id"].astype(str)
     single_gene_names = expression_data[~expression_data["entrez_gene_id"].str.contains("//")]
     multiple_gene_names = expression_data[expression_data["entrez_gene_id"].str.contains("//")]
-    split_gene_names = multiple_gene_names.assign(
-        entrez_gene_id=multiple_gene_names["entrez_gene_id"].str.split("///")
-    ).explode("entrez_gene_id")
+    split_gene_names = multiple_gene_names.assign(entrez_gene_id=multiple_gene_names["entrez_gene_id"].str.split("///")).explode("entrez_gene_id")
 
     gene_expressions = pd.concat([single_gene_names, split_gene_names], axis=0, ignore_index=True)
     gene_expressions.set_index("entrez_gene_id", inplace=True)
@@ -193,9 +183,7 @@ async def _format_determination(
     :return: A pandas DataFrame
     """
     requested_output = [requested_output] if isinstance(requested_output, Output) else requested_output
-    cohersion = (await biodbnet.db_find(values=input_values, output_db=requested_output, taxon=taxon)).drop(
-        columns=["Input Type"]
-    )
+    cohersion = (await biodbnet.db_find(values=input_values, output_db=requested_output, taxon=taxon)).drop(columns=["Input Type"])
     cohersion.columns = pd.Index(["input_value", *[o.value.replace(" ", "_").lower() for o in requested_output]])
     return cohersion
 
diff --git a/ruff.toml b/ruff.toml
index 3691008f..b65a8693 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -1,4 +1,4 @@
-line-length = 120
+line-length = 150
 extend-include = ["docs/**/*.py", "tests/**/*.py", "**/*.ipynb"]
 exclude = ["__init__.py"]
 
@@ -9,46 +9,46 @@ docstring-code-format = true
 [lint]
 # Linting rules: https://docs.astral.sh/ruff/rules/
 select = [
-    "A",        # do not use python builtins for variables or parameters; https://pypi.org/project/flake8-builtins/
-    "ASYNC",    # identify asynchronous-related problems; https://pypi.org/project/flake8-async/
-    "B",        # find likely bugs and design problems; https://pypi.org/project/flake8-bugbear/
-    "C4",       # create better list, set, & dict comprehensions; https://pypi.org/project/flake8-comprehensions/
-    "C90",      # check for complexity; https://pypi.org/project/mccabe/
-    "D",        # docstring style checker; https://pypi.org/project/pydocstyle/
-    "DOC",      # docstring linter; https://pypi.org/project/pydoclint/
-    "E",        # style guide checking; https://pypi.org/project/pycodestyle/
-    "F",        # check for errors; https://pypi.org/project/pyflakes/
-    "RUF",      # ruff-specific linting rules; https://docs.astral.sh/ruff/rules/#ruff-specific-rules-ruf
-    "FA",       # use from __future__ import annotations if needed; https://pypi.org/project/flake8-future-annotations/
-    "FURB",     # refurbish and modernize Python codebases; https://pypi.org/project/refurb/
-    "I",        # sorting imports rules; https://pypi.org/project/isort/
-    "N",        # check naming conventions; https://pypi.org/project/pep8-naming/
-    "PERF",     # check performance anti-patterns; https://pypi.org/project/perflint/
-    "PT",       # check common style issues or inconsistencies in pytest; https://pypi.org/project/flake8-pytest-style/
-    "PTH",      # use pathlib where possible; https://pypi.org/project/flake8-use-pathlib/
-    "S",        # security testing; https://pypi.org/project/flake8-bandit/
-    "SIM",      # check for code that can be simplified; https://pypi.org/project/flake8_simplify/
-    "T20",      # do not use prints in production; https://pypi.org/project/flake8-print/
-    "TRY",      # prevent Exception handling anti-patterns; https://pypi.org/project/tryceratops/
+    "A", # do not use python builtins for variables or parameters; https://pypi.org/project/flake8-builtins/
+    "ASYNC", # identify asynchronous-related problems; https://pypi.org/project/flake8-async/
+    "B", # find likely bugs and design problems; https://pypi.org/project/flake8-bugbear/
+    "C4", # create better list, set, & dict comprehensions; https://pypi.org/project/flake8-comprehensions/
+    "C90", # check for complexity; https://pypi.org/project/mccabe/
+    "D", # docstring style checker; https://pypi.org/project/pydocstyle/
+    "DOC", # docstring linter; https://pypi.org/project/pydoclint/
+    "E", # style guide checking; https://pypi.org/project/pycodestyle/
+    "F", # check for errors; https://pypi.org/project/pyflakes/
+    "RUF", # ruff-specific linting rules; https://docs.astral.sh/ruff/rules/#ruff-specific-rules-ruf
+    "FA", # use from __future__ import annotations if needed; https://pypi.org/project/flake8-future-annotations/
+    "FURB", # refurbish and modernize Python codebases; https://pypi.org/project/refurb/
+    "I", # sorting imports rules; https://pypi.org/project/isort/
+    "N", # check naming conventions; https://pypi.org/project/pep8-naming/
+    "PERF", # check performance anti-patterns; https://pypi.org/project/perflint/
+    "PT", # check common style issues or inconsistencies in pytest; https://pypi.org/project/flake8-pytest-style/
+    "PTH", # use pathlib where possible; https://pypi.org/project/flake8-use-pathlib/
+    "S", # security testing; https://pypi.org/project/flake8-bandit/
+    "SIM", # check for code that can be simplified; https://pypi.org/project/flake8_simplify/
+    "T20", # do not use prints in production; https://pypi.org/project/flake8-print/
+    "TRY", # prevent Exception handling anti-patterns; https://pypi.org/project/tryceratops/
     "UP"        # upgrade syntax for newer versions; https://pypi.org/project/pyupgrade
 ]
 ignore = [
-    "D100",     # allow undocumented public module definitions
-    "D101",     # allow undocumented public class
-    "D203",     # do not require one blank line before class docstring
-    "D213",     # first docstring line should be on the second line
-    "TRY003",   # allow exception messages outside the `Exception` class
-    "F401",     # allow unused imports
+    "D100", # allow undocumented public module definitions
+    "D101", # allow undocumented public class
+    "D203", # do not require one blank line before class docstring
+    "D213", # first docstring line should be on the second line
+    "TRY003", # allow exception messages outside the `Exception` class
+    "F401", # allow unused imports
 ]
 
 [lint.per-file-ignores]
 "tests/*" = [
-    "D101",     # allow undocumented public class
-    "D102",     # allow undocumented class method
-    "D103",     # allow undocumented public method definitions
-    "F811",     # allow redefinition of variables, required for pytest fixtures
-    "S101",     # allow use of `assert` in test files
+    "D101", # allow undocumented public class
+    "D102", # allow undocumented class method
+    "D103", # allow undocumented public method definitions
+    "F811", # allow redefinition of variables, required for pytest fixtures
+    "S101", # allow use of `assert` in test files
 ]
 "main/COMO.ipynb" = [
-    "E501",     # allow long lines
+    "E501", # allow long lines
 ]
diff --git a/tests/unit/test_rnaseq_preprocess.py b/tests/unit/test_rnaseq_preprocess.py
index c0d49c2e..56674d43 100644
--- a/tests/unit/test_rnaseq_preprocess.py
+++ b/tests/unit/test_rnaseq_preprocess.py
@@ -40,7 +40,7 @@ async def test_build_from_tab_valid_file(self):
     @pytest.mark.asyncio
     async def test_build_from_tab_invalid_file(self):
         """Validate error on invalid file."""
-        with pytest.raises(ValueError, match="Building STAR information requires a '.tab' file"):
+        with pytest.raises(ValueError, match=r"Building STAR information requires a '.tab' file"):
             await _STARinformation.build_from_tab(TestSTARInformation.invalid_data)
 
 

From e94788c5cfb1f0a94652b7e5fa31a78443a60a28 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Thu, 2 Oct 2025 12:20:26 -0500
Subject: [PATCH 330/345] Fix Single Cell Processing (#214)

---
 .github/workflows/container_build.yml         |    5 +-
 .github/workflows/continuous_integration.yml  |   10 +-
 main/COMO.ipynb                               |  209 +-
 main/como/__init__.py                         |   18 +-
 main/como/cluster_rnaseq.py                   |   63 +-
 main/como/combine_distributions.py            |  666 ++--
 main/como/create_context_specific_model.py    |  858 ++---
 main/como/data_types.py                       |  257 ++
 main/como/graph/__init__.py                   |   47 +
 main/como/merge_xomics.py                     | 1097 +++---
 main/como/proteomics/FTPManager.py            |   36 +-
 main/como/proteomics/proteomics_preprocess.py |   14 +-
 main/como/proteomics_gen.py                   |   47 +-
 main/como/proteomics_preprocessing.py         |    2 +-
 main/como/rnaseq_gen.py                       |  563 ++-
 main/como/rnaseq_preprocess.py                |  631 ++--
 main/como/types.py                            |   75 -
 main/como/utils.py                            |  276 +-
 pyproject.toml                                |   11 +-
 ruff.toml                                     |   69 +-
 tests/unit/test_data_types.py                 |   10 +
 uv.lock                                       | 3305 ++++++++---------
 22 files changed, 4111 insertions(+), 4158 deletions(-)
 create mode 100644 main/como/data_types.py
 create mode 100644 main/como/graph/__init__.py
 delete mode 100644 main/como/types.py
 create mode 100644 tests/unit/test_data_types.py

diff --git a/.github/workflows/container_build.yml b/.github/workflows/container_build.yml
index 19512c76..f24b7152 100644
--- a/.github/workflows/container_build.yml
+++ b/.github/workflows/container_build.yml
@@ -11,8 +11,11 @@ env:
 
 jobs:
   build-and-push-image:
+    # IMPORTANT: Only run the publish job when a tag starting with 'v' is pushed
+    if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v')
+
     name: Build and Push Docker Image
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-latest
     permissions:
       contents: read
       packages: write
diff --git a/.github/workflows/continuous_integration.yml b/.github/workflows/continuous_integration.yml
index bdc7b156..87acfb20 100644
--- a/.github/workflows/continuous_integration.yml
+++ b/.github/workflows/continuous_integration.yml
@@ -16,14 +16,8 @@ jobs:
       - name: Install uv
         uses: astral-sh/setup-uv@v5
 
-      - name: Create Virtual Environment
-        run: uv venv
-
-      - name: Install Jupyter
-        run: uv pip install jupyter nbconvert
-
       - name: Strip Notebook Output
-        run: uv run jupyter nbconvert --clear-output --inplace "main/COMO.ipynb"
+        run: uv tool run --from nbconvert jupyter-nbconvert --clear-output --inplace "main/COMO.ipynb"
 
       - name: Format Python Imports
         uses: astral-sh/ruff-action@v3
@@ -62,7 +56,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ "3.10" ]
+        python-version: [ "3.10", "3.11", "3.12", "3.13" ]
     steps:
       - name: Checkout
         uses: actions/checkout@v4
diff --git a/main/COMO.ipynb b/main/COMO.ipynb
index 5bc09cb6..17c1c54b 100644
--- a/main/COMO.ipynb
+++ b/main/COMO.ipynb
@@ -53,7 +53,7 @@
     "    3. Compare results between perturbed and unperturbed models (i.e., knocked-out models vs non-knocked-out models)\n",
     "    4. Integrate with disease genes and create a score of drug targets"
    ],
-   "id": "90fba8b56a999517"
+   "id": "7062fd81cf966d2c"
   },
   {
    "metadata": {},
@@ -223,7 +223,7 @@
     "    </tbody>\n",
     "</table>"
    ],
-   "id": "dcc8d38caab63fab"
+   "id": "7dd2ed04bffa67f7"
   },
   {
    "metadata": {},
@@ -233,17 +233,47 @@
    "source": [
     "from pathlib import Path\n",
     "\n",
+    "\n",
+    "def get_notebook_dir() -> Path:\n",
+    "    \"\"\"Get the directory of the COMO.ipynb notebook.\n",
+    "\n",
+    "    This is used to set the proper input and output paths for the rest of the notebook\n",
+    "    \"\"\"\n",
+    "    current_dir = Path().cwd()\n",
+    "\n",
+    "    while True:\n",
+    "        files = [i.name for i in current_dir.iterdir()]\n",
+    "        if \"COMO.ipynb\" in files:\n",
+    "            return current_dir\n",
+    "        if current_dir.as_posix() == \"/\":\n",
+    "            raise FileNotFoundError(\"Root directory could not be determined; unable to find 'COMO.ipynb'\")\n",
+    "        current_dir = current_dir.parent"
+   ],
+   "id": "5c66beeb317e91d2"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "from como.data_types import RNAType\n",
     "from como.rnaseq_preprocess import rnaseq_preprocess\n",
-    "from como.types import RNAPrepMethod\n",
     "\n",
+    "notebook_dir: Path = get_notebook_dir()\n",
     "taxon_id = 9606\n",
     "context_names = [\"naiveB\"]\n",
     "\n",
     "gene_info_filepath = {context: Path(f\"data/results/{context}/gene_info.csv\") for context in context_names}\n",
     "como_context_dir = {context: Path(f\"data/COMO_input/{context}\") for context in context_names}\n",
-    "trna_matrix_filepath = {context: Path(f\"data/results/{context}/total-rna/totalrna_{context}.csv\") for context in context_names}\n",
-    "mrna_matrix_filepath = {context: Path(f\"data/results/{context}/polya-rna/polyarna_{context}.csv\") for context in context_names}\n",
-    "proteomics_matrix_filepath = {context: Path(f\"data/data_matrices/{context}/protein_abundance_{context}.csv\") for context in context_names}\n",
+    "trna_matrix_filepath = {context: Path(f\"data/results/{context}/total-rna/totalrna_{context}.csv\") for context in\n",
+    "                        context_names}\n",
+    "mrna_matrix_filepath = {context: Path(f\"data/results/{context}/polya-rna/polyarna_{context}.csv\") for context in\n",
+    "                        context_names}\n",
+    "proteomics_matrix_filepath = {context: Path(f\"data/data_matrices/{context}/protein_abundance_{context}.csv\") for context\n",
+    "                              in context_names}\n",
     "\n",
     "trna_metadata_filepath = Path(\"data/config_sheets/trna_config.xlsx\")\n",
     "mrna_metadata_filepath = Path(\"data/config_sheets/mrna_config.xlsx\")\n",
@@ -251,10 +281,15 @@
     "\n",
     "# No single-cell data is provided by default; COMO accepts single-cell data in CSV or h5ad format\n",
     "# If you are using single-cell data, adjust the following lines accordingly\n",
-    "scrna_matrix_filepath = {context: Path(f\"data/results/{context}/scrna/scrna_{context}.csv\") for context in context_names}\n",
-    "# scrna_matrix_filepath = [Path(f\"data/results/{context}/scrna/scrna_{context}.h5ad\") for context in context_names]\n"
+    "scrna_matrix_filepath = {context: Path(f\"data/results/{context}/scrna/scrna_{context}.csv\") for context in\n",
+    "                         context_names}\n",
+    "# scrna_matrix_filepath = [Path(f\"data/results/{context}/scrna/scrna_{context}.h5ad\") for context in context_names]\n",
+    "\n",
+    "trna_metadata_filepath = Path(notebook_dir / \"data/config_sheets/trna_config.xlsx\")\n",
+    "mrna_metadata_filepath = Path(notebook_dir / \"data/config_sheets/mrna_config.xlsx\")\n",
+    "proteomics_metadata_filepath = Path(notebook_dir / \"data/config_sheets/proteomics_config.xlsx\")\n"
    ],
-   "id": "fd4c387eab48e9ee"
+   "id": "e6e217041afb37f6"
   },
   {
    "metadata": {},
@@ -266,7 +301,7 @@
     "- `taxon_id`: The [NCBI Taxon ID](https://www.ncbi.nlm.nih.gov/taxonomy) to use\n",
     "- `preprocess_mode`: This should be set to `\"create-matrix\"` if you are **not** providing a matrix, otherwise set it to `\"provide-matrix\"`"
    ],
-   "id": "24a8475316ff6d70"
+   "id": "c1755a5725908a92"
   },
   {
    "metadata": {},
@@ -283,15 +318,15 @@
     "        como_context_dir=como_context_dir[context],\n",
     "        input_matrix_filepath=None,\n",
     "        output_gene_info_filepath=gene_info_filepath[context],\n",
-    "        output_trna_config_filepath=trna_metadata_filepath,\n",
+    "        output_trna_metadata_filepath=trna_metadata_filepath,\n",
     "        output_trna_count_matrix_filepath=trna_matrix_filepath[context],\n",
-    "        output_mrna_config_filepath=mrna_metadata_filepath,\n",
+    "        output_mrna_metadata_filepath=mrna_metadata_filepath,\n",
     "        output_mrna_count_matrix_filepath=mrna_matrix_filepath[context],\n",
     "        cache=True,\n",
     "        log_level=\"INFO\",\n",
     "    )"
    ],
-   "id": "cb04960f17cc9b75"
+   "id": "4f0a2906b10b0234"
   },
   {
    "metadata": {},
@@ -324,7 +359,7 @@
     "\n",
     "This method is not recommended, as zFPKM is much more robust for a similar level of \"hands-off\" model building\n"
    ],
-   "id": "4bd864328c93b27a"
+   "id": "9593ba96306b1633"
   },
   {
    "metadata": {},
@@ -345,7 +380,7 @@
     "#### Single Cell RNA Sequencing\n",
     "While the Snakemake pipeline does not yet support single-cell alignment, and COMO does not yet support automated configuration file and counts matrix file creation for single-cell alignment output from STAR, it is possible to use single-cell data to create a model with COMO. Because normalization strategies can be applied to single-cell data in the same way it is applied to bulk RNA sequencing, `como/rnaseq_gen.py` can be used with a provided counts matrix and configuration file, from [Step 1](Step-1:-Initialize-and-Preprocess-RNA-seq-data), above. Just like `\"total\"` and `\"mRNA\"`, `como/rnaseq_gen.py` can be executed with `\"SC\"` as the \"`--library-prep`\" argument to help COMO differentiate it from any bulk RNA sequencing data if multiple strategies are being used."
    ],
-   "id": "e99e612919298aa8"
+   "id": "1a19054351d999ca"
   },
   {
    "metadata": {},
@@ -364,7 +399,7 @@
     "- `min_zfpkm`: The cutoff for Counts-Per-Million filtering\n",
     "- `prep_method`: The library method used for preparation. Options are: `\"total\"`, `\"mRNA\"`, or `\"SC\"`,\n"
    ],
-   "id": "8e550961a4a443b3"
+   "id": "b2db83f80dd55833"
   },
   {
    "metadata": {},
@@ -378,29 +413,31 @@
     "high_confidence_replicate_ratio = 1.0\n",
     "batch_ratio = 0.75\n",
     "high_confidence_batch_ratio = 1.0\n",
-    "technique = FilteringTechnique.zfpkm\n",
+    "technique = FilteringTechnique.ZFPKM\n",
     "cutoff = -3\n",
     "\n",
     "for context in context_names:\n",
     "    if context not in trna_matrix_filepath:\n",
     "        continue\n",
+    "    output_zscore_norm_filepath = Path(get_notebook_dir() / f\"data/results/{context}/z_score_normalization.csv\")\n",
     "    await rnaseq_gen(\n",
     "        context_name=context,\n",
     "        input_rnaseq_filepath=trna_matrix_filepath[context],\n",
     "        input_gene_info_filepath=gene_info_filepath[context],\n",
-    "        output_rnaseq_filepath=trna_matrix_filepath[context],\n",
-    "        prep=RNAPrepMethod.TOTAL,\n",
-    "        taxon=taxon_id,\n",
-    "        input_metadata_filepath=trna_metadata_filepath,\n",
+    "        output_boolean_activity_filepath=trna_matrix_filepath[context],\n",
+    "        prep=RNAType.TRNA,\n",
+    "        taxon_id=taxon_id,\n",
+    "        input_metadata_filepath_or_df=trna_metadata_filepath,\n",
     "        replicate_ratio=replicate_ratio,\n",
     "        high_replicate_ratio=high_confidence_replicate_ratio,\n",
     "        batch_ratio=batch_ratio,\n",
     "        high_batch_ratio=high_confidence_batch_ratio,\n",
     "        technique=technique,\n",
-    "        cutoff=cutoff\n",
+    "        cutoff=cutoff,\n",
+    "        output_zscore_normalization_filepath=output_zscore_norm_filepath,\n",
     "    )"
    ],
-   "id": "d98c023e7738eb64"
+   "id": "e26de1f2ba0637cd"
   },
   {
    "metadata": {},
@@ -420,7 +457,7 @@
     "- `min_zfpkm`: The cutoff for Counts-Per-Million filtering\n",
     "- `prep_method`: The library method used for preparation. Options are: `\"total\"`, `\"mRNA\"`, or `\"SC\"`,\n"
    ],
-   "id": "28848c3d315fa78c"
+   "id": "6d11a51afe48123e"
   },
   {
    "metadata": {},
@@ -434,7 +471,7 @@
     "high_confidence_replicate_ratio = 1.0\n",
     "batch_ratio = 0.75\n",
     "high_confidence_batch_ratio = 1.0\n",
-    "technique = FilteringTechnique.zfpkm\n",
+    "technique = FilteringTechnique.ZFPKM\n",
     "cutoff = -3\n",
     "\n",
     "for context in context_names:\n",
@@ -444,19 +481,20 @@
     "        context_name=context,\n",
     "        input_rnaseq_filepath=mrna_matrix_filepath[context],\n",
     "        input_gene_info_filepath=gene_info_filepath[context],\n",
-    "        output_rnaseq_filepath=mrna_matrix_filepath[context],\n",
-    "        prep=RNAPrepMethod.MRNA,\n",
-    "        taxon=taxon_id,\n",
-    "        input_metadata_filepath=mrna_metadata_filepath,\n",
+    "        output_boolean_activity_filepath=mrna_matrix_filepath[context],\n",
+    "        prep=RNAType.MRNA,\n",
+    "        taxon_id=taxon_id,\n",
+    "        input_metadata_filepath_or_df=mrna_metadata_filepath,\n",
     "        replicate_ratio=replicate_ratio,\n",
     "        high_replicate_ratio=high_confidence_replicate_ratio,\n",
     "        batch_ratio=batch_ratio,\n",
     "        high_batch_ratio=high_confidence_batch_ratio,\n",
     "        technique=technique,\n",
-    "        cutoff=cutoff\n",
+    "        cutoff=cutoff,\n",
+    "        output_zscore_normalization_filepath=None,\n",
     "    )"
    ],
-   "id": "3e87f9890bc76e43"
+   "id": "16b6af66244b601e"
   },
   {
    "metadata": {},
@@ -476,7 +514,7 @@
     "- `min_zfpkm`: The cutoff for Counts-Per-Million filtering\n",
     "- `prep_method`: The library method used for preparation. Options are: `\"total\"`, `\"mRNA\"`, or `\"scrna\"`,\n"
    ],
-   "id": "bfa9e2cd4152f961"
+   "id": "9bbdfcb6c3f26a73"
   },
   {
    "metadata": {},
@@ -490,7 +528,7 @@
     "high_confidence_replicate_ratio = 1.0\n",
     "batch_ratio = 0.75\n",
     "high_confidence_batch_ratio = 1.0\n",
-    "technique = FilteringTechnique.umi\n",
+    "technique = FilteringTechnique.UMI\n",
     "cutoff = -3\n",
     "\n",
     "for context in context_names:\n",
@@ -500,19 +538,20 @@
     "        context_name=context,\n",
     "        input_rnaseq_filepath=scrna_matrix_filepath[context],\n",
     "        input_gene_info_filepath=gene_info_filepath[context],\n",
-    "        output_rnaseq_filepath=scrna_matrix_filepath[context],\n",
-    "        prep=RNAPrepMethod.SCRNA,\n",
-    "        taxon=taxon_id,\n",
-    "        input_metadata_filepath=Path(\"./data/config_sheets/scrna_config.xlsx\"),\n",
+    "        output_boolean_activity_filepath=scrna_matrix_filepath[context],\n",
+    "        prep=RNAType.SCRNA,\n",
+    "        taxon_id=taxon_id,\n",
+    "        input_metadata_filepath_or_df=Path(\"./data/config_sheets/scrna_config.xlsx\"),\n",
     "        replicate_ratio=replicate_ratio,\n",
     "        high_replicate_ratio=high_confidence_replicate_ratio,\n",
     "        batch_ratio=batch_ratio,\n",
     "        high_batch_ratio=high_confidence_batch_ratio,\n",
     "        technique=technique,\n",
-    "        cutoff=cutoff\n",
+    "        cutoff=cutoff,\n",
+    "        output_zscore_normalization_filepath=None\n",
     "    )"
    ],
-   "id": "c45085c6c12d9a3c"
+   "id": "416c98bd32d22a5f"
   },
   {
    "metadata": {},
@@ -529,7 +568,7 @@
     "- `high_batch_ratio`: The ratio required before a gene is considered \"high-confidence\" in the study\n",
     "- `quantile`: The cutoff Transcripts-Per-Million quantile for filtering"
    ],
-   "id": "f01f21670b1e6956"
+   "id": "c14f51d9a3eb996c"
   },
   {
    "metadata": {},
@@ -556,7 +595,7 @@
     "        quantile=25,\n",
     "    )"
    ],
-   "id": "dcbae45189dd2eba"
+   "id": "c9e99ca8554c1d6"
   },
   {
    "metadata": {},
@@ -582,7 +621,7 @@
     "- `n_neighbors_context`: N nearest neighbors for context clustering. The default is `\"default\"`, which is the total number of contexts\n",
     "- `seed`: The random seed for clustering algorithm initialization. If not specified, `np.random.randint(0, 100000)` is used"
    ],
-   "id": "354abde9f52ff9cf"
+   "id": "8e69203fbb1c1e37"
   },
   {
    "metadata": {},
@@ -626,7 +665,7 @@
     "\n",
     "!{cmd}"
    ],
-   "id": "be527c98a8523323"
+   "id": "d59fdb701aaac1d7"
   },
   {
    "metadata": {},
@@ -666,7 +705,7 @@
     "\n",
     "Each of the \"weights\" (`total_rna_weight`, `mrna_weight`, etc.) are used to place a significance on each method. Becuase there are many steps in the Dogma from transcription to translation, the gene expression as seen by total RNA or mRNA sequencing may not be representative of the gene's protein expression, and this its metabolic impact. Because of this, you are able to weight each source more (or less) than another."
    ],
-   "id": "db0cfbec103d7d35"
+   "id": "85a1f953f0d63449"
   },
   {
    "metadata": {},
@@ -674,36 +713,49 @@
    "outputs": [],
    "execution_count": null,
    "source": [
-    "expression_requirement = 3\n",
-    "requirement_adjust = \"regressive\"\n",
+    "from como.merge_xomics import AdjustmentMethod, merge_xomics\n",
+    "\n",
     "total_rna_weight = 6\n",
     "mrna_weight = 6\n",
     "single_cell_weight = 6\n",
     "proteomics_weight = 10\n",
+    "minimum_source_expression = 1\n",
+    "expression_requirement = 1\n",
+    "requirement_adjustment_method = AdjustmentMethod.REGRESSIVE\n",
+    "force_activate_high_confidence = False\n",
+    "adjust_for_na_sources = False\n",
+    "merge_zfpkm_distrubution = True\n",
+    "keep_transcriptomics_score = True\n",
     "\n",
-    "# fmt: off\n",
-    "cmd = \" \".join(\n",
-    "    [\n",
-    "        \"python3\", \"como/merge_xomics.py\",\n",
-    "        \"--merge-zfpkm-distribution\",\n",
-    "        \"--total-rnaseq-config-file\", trnaseq_config_file,  # noqa: F821\n",
-    "        \"--mrnaseq-config-file\", mrnaseq_config_file,  # noqa: F821\n",
-    "        # \"--scrnaseq-config-file\", scrnaseq_config_file,  # If using single-cell data, uncomment the start of this line\n",
-    "        # \"--proteomics-config-file\", proteomics_config_file,  # If using proteomics data, uncomment the start of this line\n",
-    "        \"--requirement-adjust\", requirement_adjust,\n",
-    "        \"--expression-requirement\", str(expression_requirement),\n",
-    "        \"--total-rnaseq-weight\", str(total_rna_weight),\n",
-    "        \"--mrnaseq-weight\", str(mrna_weight),\n",
-    "        \"--single-cell-rnaseq-weight\", str(single_cell_weight),\n",
-    "        \"--protein-weight\", str(proteomics_weight),\n",
-    "        \"--no-high-confidence\",\n",
-    "    ]\n",
-    ")\n",
-    "# fmt: on\n",
-    "\n",
-    "!{cmd}"
+    "for context in context_names:\n",
+    "    await merge_xomics(\n",
+    "        context_name=context,\n",
+    "        taxon_id=taxon_id,\n",
+    "        trna_matrix_or_filepath=trna_matrix_filepath[context],\n",
+    "        mrna_matrix_or_filepath=mrna_matrix_filepath[context],\n",
+    "        scrna_matrix_or_filepath=None,  # scrna_matrix_filepath[context],\n",
+    "        proteomic_matrix_or_filepath=None,  # proteomics_matrix_filepath[context],\n",
+    "        trna_weight=total_rna_weight,\n",
+    "        mrna_weight=mrna_weight,\n",
+    "        scrna_weight=single_cell_weight,\n",
+    "        proteomic_weight=proteomics_weight,\n",
+    "        minimum_source_expression=minimum_source_expression,\n",
+    "        expression_requirement=expression_requirement,\n",
+    "        adjust_method=requirement_adjustment_method,\n",
+    "        force_activate_high_confidence=force_activate_high_confidence,\n",
+    "        adjust_for_na=adjust_for_na_sources,\n",
+    "        merge_zfpkm_distribution=merge_zfpkm_distrubution,\n",
+    "        output_merge_activity_filepath=Path(f\"data/results/{context}/ActiveGenes_{context}_Merged.csv\"),\n",
+    "        output_transcriptomic_details_filepath=Path(f\"data/results/{context}/TranscriptomicDetails_{context}.csv\"),\n",
+    "        output_trna_activity_filepath=Path(f\"data/results/{context}/total-rna/trna_activity_{context}.csv\"),\n",
+    "        output_mrna_activity_filepath=Path(f\"data/results/{context}/mrna/mrna_activity_{context}.csv\"),\n",
+    "        output_scrna_activity_filepath=Path(f\"data/results/{context}/scrna/scrna_activity_{context}.csv\"),\n",
+    "        output_proteomic_activity_filepath=Path(f\"data/results/{context}/proteomics/proteomic_activity_{context}.csv\"),\n",
+    "        output_final_model_scores_filepath=Path(f\"data/results/{context}/model_scores_{context}.csv\"),\n",
+    "        output_figure_dirpath=Path(f\"data/results/{context}/figures\")\n",
+    "    )"
    ],
-   "id": "75b84df4d88d7cbe"
+   "id": "42b1fdb7d2cdcf0e"
   },
   {
    "metadata": {},
@@ -770,7 +822,7 @@
     "- `force_reactions_filename`: The filename of the force reactions to be used. Force reactions will (as the name implies) force the optimizer to use these reactions, **no matter their expression**\n",
     "- `exclude_reactions_filename`: The filename of reactions to exclude from the model, no matter their expression"
    ],
-   "id": "30da70d405a80d23"
+   "id": "6fb29586aa43edf5"
   },
   {
    "metadata": {},
@@ -779,7 +831,6 @@
    "execution_count": null,
    "source": [
     "import json\n",
-    "import os\n",
     "from pathlib import Path\n",
     "\n",
     "from como.project import Config\n",
@@ -839,7 +890,7 @@
     "        # fmt: on\n",
     "        !{cmd}"
    ],
-   "id": "19918f482629d4fe"
+   "id": "b24c246aefd4c71"
   },
   {
    "metadata": {},
@@ -864,7 +915,7 @@
     "- `exampleTissue`: This is the name of the tissue context\n",
     "- `ALGORITHM`: This is the algorithm (`recon_algorithm`) used in the above model creation step\n"
    ],
-   "id": "38640adc60fe1909"
+   "id": "efe6949ce8148d42"
   },
   {
    "metadata": {},
@@ -941,7 +992,7 @@
     "\n",
     "        !{cmd}"
    ],
-   "id": "ef639305627a4a4"
+   "id": "5a31663187113c5d"
   },
   {
    "metadata": {},
@@ -960,7 +1011,7 @@
     "- `data_source`: The datasource you are using for disease analysis. This should be`\"rnaseq\"`\n",
     "- `taxon_id`: The [NCBI Taxon ID](https://www.ncbi.nlm.nih.gov/taxonomy) to use for disease analysis"
    ],
-   "id": "ad06b9e81d3d5f39"
+   "id": "14019b16b37a1aa8"
   },
   {
    "metadata": {},
@@ -974,7 +1025,6 @@
     "data_source = \"rnaseq\"\n",
     "taxon_id = \"human\"\n",
     "\n",
-    "\n",
     "for context_name in stringlist_to_list(context_names):\n",
     "    disease_config_file = f\"disease_data_inputs_{context_name}.xlsx\"\n",
     "\n",
@@ -992,7 +1042,7 @@
     "\n",
     "    !{cmd}"
    ],
-   "id": "c9d0e6dfd35adefb"
+   "id": "b31e5ca8a1bd8ce5"
   },
   {
    "metadata": {},
@@ -1027,7 +1077,7 @@
     "\n",
     "- `solver`: The solver you would like to use. Available options are `\"gurobi\"` or `\"glpk\"`\n"
    ],
-   "id": "7093fbd5a6551c18"
+   "id": "320beb32340a1b9e"
   },
   {
    "metadata": {},
@@ -1048,7 +1098,6 @@
     "}\n",
     "sovler = \"gurobi\"\n",
     "\n",
-    "\n",
     "config = Config()\n",
     "\n",
     "drug_raw_file = \"Repurposing_Hub_export.txt\"\n",
@@ -1076,7 +1125,7 @@
     "                tissue_specific_model_filepath = model_files[context]\n",
     "            else:\n",
     "                tissue_specific_model_filepath = (\n",
-    "                    config.data_dir / \"results\" / context / f\"{context}_SpecificModel_{recon_algorithm}.mat\"\n",
+    "                        config.data_dir / \"results\" / context / f\"{context}_SpecificModel_{recon_algorithm}.mat\"\n",
     "                )\n",
     "\n",
     "            # fmt: off\n",
@@ -1099,7 +1148,7 @@
     "            cmd = \" \".join(cmd)\n",
     "            !{cmd}"
    ],
-   "id": "281a24eba5e06fc9"
+   "id": "46d37e244a62c039"
   }
  ],
  "metadata": {},
diff --git a/main/como/__init__.py b/main/como/__init__.py
index c6ee72e2..c84ef56e 100644
--- a/main/como/__init__.py
+++ b/main/como/__init__.py
@@ -1,11 +1,13 @@
-import pandas as pd
-
-from como.project import Config
+from como.data_types import AdjustmentMethod, Algorithm, CobraCompartments, FilteringTechnique, LogLevel, Solver
 from como.utils import stringlist_to_list
 
-__all__ = ["stringlist_to_list", "Config"]
+__all__ = [
+    "AdjustmentMethod",
+    "Algorithm",
+    "CobraCompartments",
+    "FilteringTechnique",
+    "LogLevel",
+    "Solver",
+    "stringlist_to_list",
+]
 __version__ = "1.11.1"
-
-
-def return_placeholder_data() -> pd.DataFrame:
-    return pd.DataFrame(data=0, index=pd.Index(data=[0], name="entrez_gene_id"), columns=["expressed", "top"])
diff --git a/main/como/cluster_rnaseq.py b/main/como/cluster_rnaseq.py
index fc3a9f48..1078b926 100644
--- a/main/como/cluster_rnaseq.py
+++ b/main/como/cluster_rnaseq.py
@@ -7,7 +7,8 @@
 
 import numpy as np
 
-from como.utils import stringlist_to_list
+from como.data_types import LogLevel
+from como.utils import _log_and_raise_error, stringlist_to_list
 
 
 @dataclass
@@ -34,37 +35,77 @@ def __post_init__(self):  # noqa: C901, ignore too complex
             self.seed = np.random.randint(0, 100_000)
 
         if (isdigit(self.min_active_count) and int(self.min_active_count) < 0) or self.min_active_count != "default":
-            raise ValueError("--min-count must be either 'default' or an integer > 0")
+            _log_and_raise_error(
+                "min_active_count must be either 'default' or an integer > 0",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         if (isdigit(self.quantile) and 0 > int(self.quantile) > 100) or self.quantile != "default":
-            raise ValueError("--quantile must be either 'default' or an integer between 0 and 100")
+            _log_and_raise_error(
+                "quantile must be either 'default' or an integer between 0 and 100",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         if (isdigit(self.replicate_ratio) and 0 > self.replicate_ratio > 1.0) or self.replicate_ratio != "default":
-            raise ValueError("--rep-ratio must be either 'default' or a float between 0 and 1")
+            _log_and_raise_error(
+                "--rep-ratio must be either 'default' or a float between 0 and 1",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         if (isdigit(self.batch_ratio) and 0 > self.batch_ratio > 1.0) or self.batch_ratio != "default":
-            raise ValueError("--batch-ratio must be either 'default' or a float between 0 and 1")
+            _log_and_raise_error(
+                "--batch-ratio must be either 'default' or a float between 0 and 1",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         if self.filtering_technique.lower() not in {"quantile", "tpm", "cpm", "zfpkm"}:
-            raise ValueError("--technique must be either 'quantile', 'tpm', 'cpm', 'zfpkm'")
+            _log_and_raise_error(
+                "--technique must be either 'quantile', 'tpm', 'cpm', 'zfpkm'",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         if self.filtering_technique.lower() == "tpm":
             self.filtering_technique = "quantile"
 
         if self.cluster_algorithm.lower() not in {"mca", "umap"}:
-            raise ValueError("--clust_algo must be either 'mca', 'umap'")
+            _log_and_raise_error(
+                "--clust_algo must be either 'mca', 'umap'",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         if 0 > self.min_distance > 1.0:
-            raise ValueError("--min_dist must be a float between 0 and 1")
+            _log_and_raise_error(
+                "--min_dist must be a float between 0 and 1",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         if (isdigit(self.num_replicate_neighbors) and self.num_replicate_neighbors < 1) or self.num_replicate_neighbors != "default":
-            raise ValueError("--n-neighbors-rep must be either 'default' or an integer > 1")
+            _log_and_raise_error(
+                "--n-neighbors-rep must be either 'default' or an integer > 1",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         if (isdigit(self.num_batch_neighbors) and self.num_batch_neighbors < 1) or self.num_batch_neighbors != "default":
-            raise ValueError("--n-neighbors-batch must be either 'default' or an integer > 1")
+            _log_and_raise_error(
+                "--n-neighbors-batch must be either 'default' or an integer > 1",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         if (isdigit(self.num_context_neighbors) and self.num_context_neighbors < 1) or self.num_context_neighbors != "default":
-            raise ValueError("--n-neighbors-context must be either 'default' or an integer > 1")
+            _log_and_raise_error(
+                "--n-neighbors-context must be either 'default' or an integer > 1",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
 
 def _parse_args() -> _Arguments:
diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 68939dda..2d2063df 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -1,429 +1,269 @@
+from __future__ import annotations
+
+import asyncio
 from pathlib import Path
 
 import numpy as np
 import pandas as pd
-import plotly.express as px
 from loguru import logger
 
-
-def _get_batch_name(x):
-    return Path(x).stem
-
-
-def _parse_contexts_zfpkm(wd, contexts, prep):
-    batches = {}
-    for context in contexts:
-        files = (wd / context / prep).glob(f"rnaseq_{prep}_{context}.csv")
-        batches[context] = [_get_batch_name(f) for f in files]
-    return batches
-
-
-def _parse_contexts_zumi(wd, contexts, prep):
-    batches = {}
-    for context in contexts:
-        files = (wd / context / prep).glob(f"*{prep}*.csv")
-        batches[context] = [_get_batch_name(f) for f in files]
-    return batches
-
-
-def _parse_contexts_zscore_prot(wd, contexts):
-    batches = {}
-    for context in contexts:
-        files = (wd / context / "proteomics").glob("protein_zscore_Matrix_*.csv")
-        batches[context] = [_get_batch_name(f) for f in files]
-    return batches
-
-
-def _merge_batch(wd, context, batch):
-    files = list(wd.glob(f"*{batch}*"))
-    nrep = []
-    if not files:
-        raise ValueError(f"No files found for {context} - {batch}")
-
-    for f in files:
-        zmat = pd.read_csv(f)
-        zmat.columns = pd.Index([c.lower() for c in zmat.columns])
-        zmat["entrez_gene_id"] = zmat["entrez_gene_id"].astype(str).str.split("//").str[0]
-
-        zmat = zmat.astype({col: float for col in zmat.columns if col != "entrez_gene_id"})
-        zmat = zmat.astype({"entrez_gene_id": str})
-        zmat = zmat.groupby("entrez_gene_id").max().reset_index()
-        zmat = zmat.dropna()
-
-        nrep.append(zmat.shape[1] - 1)
-        entrez_gene = zmat["entrez_gene_id"]
-        rep_names = zmat.columns
-        zmat = pd.concat([zmat[col] for col in zmat.columns[1:]], axis=1)
-        zmat = pd.concat([entrez_gene, zmat], axis=1)
-        zmat = zmat.dropna()
-        zmat.columns = rep_names
-
-        stack_df = pd.concat(
-            [pd.DataFrame({"entrez_gene_id": zmat["entrez_gene_id"], "zscore": zmat[col].astype(float), "source": col}) for col in zmat.columns[1:]]
-        )
-
-        plot_name_png = wd / "figures" / f"plot_{context}_{Path(f).stem}.png"
-
-        fig = px.histogram(
-            stack_df,
-            x="zscore",
-            color="source",
-            nbins=100,  # Adjust as needed
-            marginal="rug",
-            title=f"Z-score Distribution for {context} - {Path(f).stem}",
-        )
-
-        fig.update_layout(xaxis_title="Z-score", yaxis_title="Frequency", font={"family": "sans-serif", "size": 12})
-
-        # Simplified plot for many sources (optional)
-        if len(stack_df["source"].unique()) > 10:
-            fig.update_layout(showlegend=False)
-
-        plot_name_png.parent.mkdir(parents=True, exist_ok=True)
-        fig.write_image(plot_name_png)
-
-    return zmat, nrep
-
-
-def _combine_batch_zdistro(wd, context, batch, zmat):
-    plot_name_png = wd / "figures" / f"plot_{context}_{batch}_combine_distro.png"
-
-    def weighted_z(x):
-        floor_score = -6
-        ceil_score = 6
-        x = np.array(x, dtype=float)
-        result = np.sum(x) / np.sqrt(len(x))
-        return np.clip(result, floor_score, ceil_score)
-
-    combine_z = zmat
-    if zmat.shape[1] > 2:
-        combine_z = np.apply_along_axis(weighted_z, axis=1, arr=zmat.iloc[:, 1:].values)
-        merge_df = pd.concat([zmat, pd.Series(combine_z, name="combined")], axis=1)
-        combine_z = pd.DataFrame({"entrez_gene_id": zmat["entrez_gene_id"].astype(str), "combine_z": combine_z})
-
-        stack_df = pd.concat(
-            [
-                pd.DataFrame({"entrez_gene_id": merge_df["entrez_gene_id"], "zscore": merge_df[col].astype(float), "source": col})
-                for col in merge_df.columns[1:]
-            ]
-        )
-
-        # Simplified plot for many sources (optional)
-        if len(stack_df["source"].unique()) > 10:
-            stack_df = stack_df[stack_df["source"] == "combined"]
-
-        fig = px.histogram(
-            stack_df,
-            x="zscore",
-            color="source",
-            nbins=100,
-            marginal="rug",
-            title=f"Combined Z-score Distribution for {context} - {batch}",
-        )
-        fig.update_layout(xaxis_title="Z-score", yaxis_title="Frequency", font={"family": "sans-serif", "size": 12})
-        fig.write_image(plot_name_png)
-
-    return combine_z
-
-
-def _combine_context_zdistro(wd, context, n_reps, zmat):
-    plot_name_png = wd / "figures" / f"plot_{context}_combine_batches_distro.png"
-
-    def weighted_z(x, n_reps):
-        floor_score = -6
-        ceil_score = 6
-        x = np.array(x, dtype=float)
-        nas = np.where(np.isnan(x))[0]
-        if len(nas) > 0:
-            x = np.delete(x, nas)
-            n_reps = np.delete(n_reps, nas)
-        weights = n_reps / np.sum(n_reps)
-        numer = np.sum(weights * x)
-        denom = np.sqrt(np.sum(weights**2))
-        result = numer / denom
-        return np.clip(result, floor_score, ceil_score)
-
-    if zmat.shape[1] > 2:
-        combine_z = np.apply_along_axis(weighted_z, axis=1, arr=zmat.iloc[:, 1:].values, n_reps=n_reps)
-        merge_df = pd.concat([zmat, pd.Series(combine_z, name="combined")], axis=1)
-        combine_z = pd.DataFrame({"entrez_gene_id": zmat["entrez_gene_id"].astype(str), "combine_z": combine_z})
-
-        stack_df = pd.concat(
-            [
-                pd.DataFrame({"entrez_gene_id": merge_df["entrez_gene_id"], "zscore": merge_df[col].astype(float), "source": col})
-                for col in merge_df.columns[1:]
-            ]
-        )
-
-        fig = px.histogram(
-            stack_df,
-            x="zscore",
-            color="source",
-            nbins=100,  # Adjust as needed
-            marginal="rug",
-            title=f"Combined Batches Z-score Distribution for {context}",
-        )
-
-        fig.update_layout(xaxis_title="Z-score", yaxis_title="Frequency", font=dict(family="sans-serif", size=12))  # noqa: C408
-
-        fig.write_image(plot_name_png)
-
-    else:
-        combine_z = zmat
-        combine_z.columns = ["entrez_gene_id", "combine_z"]
-
-    return combine_z
-
-
-def _combine_omics_zdistros(
-    wd,
-    context,
-    comb_batches_z_trna,
-    comb_batches_z_mrna,
-    comb_batches_z_scrna,
-    comb_batches_z_prot,
-    trna_weight: int,
-    mrna_weight: int,
-    scrna_weight: int,
-    proteomics_weight: int,
-    keep_gene_scores=True,
+from como.data_types import (
+    GeneIdentifier,
+    SourceTypes,
+    _BatchEntry,
+    _BatchNames,
+    _CombineOmicsInput,
+    _InputMatrices,
+    _OutputCombinedSourceFilepath,
+    _SourceWeights,
+)
+from como.utils import (
+    _num_columns,
+)
+
+
+async def _combine_z_distribution_for_batch(
+    context_name: str,
+    batch: _BatchEntry,
+    matrix: pd.DataFrame,
+    source: SourceTypes,
+    output_combined_matrix_filepath: Path,
+    output_figure_dirpath: Path,
+    weighted_z_floor: int,
+    weighted_z_ceiling: int,
+) -> pd.DataFrame:
+    output_combined_matrix_filepath.parent.mkdir(parents=True, exist_ok=True)
+    output_figure_dirpath.mkdir(parents=True, exist_ok=True)
+
+    logger.trace(
+        f"Combining z-score distributions: batch #{batch.batch_num}, "
+        f"samples: {len(batch.sample_names)}, "
+        f"source: '{source.value}', "
+        f"context: '{context_name}'"
+    )
+    if _num_columns(matrix) < 2:
+        logger.trace(f"A single sample exists for batch '{batch.batch_num}'. Returning as-is because no additional combining can be done")
+        return matrix
+
+    values = matrix.iloc[:, 1:].values
+    weighted_matrix = np.sum(values, axis=1) / np.sqrt(values.shape[1])
+    weighted_matrix = np.clip(weighted_matrix, weighted_z_floor, weighted_z_ceiling).astype(np.int8)
+
+    merge_df = pd.concat([matrix, pd.Series(weighted_matrix, name="combined")], axis=1)
+    weighted_matrix = pd.DataFrame(
+        {
+            "ensembl_gene_id": matrix["ensembl_gene_id"],
+            "combine_z": weighted_matrix,
+        },
+    )
+    stack_df = pd.melt(
+        merge_df,
+        id_vars=["ensembl_gene_id"],
+        # Get all columns except ensembl_gene_id
+        value_vars=[col for col in merge_df.columns if col not in GeneIdentifier._member_map_],
+        var_name="source",
+        value_name="zscore",
+    )
+    if len(stack_df["source"].unique()) > 10:
+        stack_df = stack_df[stack_df["source"] == "combined"]
+
+    # graph_zscore_distribution(
+    #     stack_df,
+    #     title=f"Combined Z-score Distribution for {context_name} - batch #{batch.batch_num}",
+    #     output_filepath=output_figure_dirpath
+    #     / f"{context_name}_{source.value}_batch{batch.batch_num}_combined_zscore_distribution.pdf",
+    # )
+
+    weighted_matrix.columns = ["ensembl_gene_id", batch.batch_num]
+    weighted_matrix.to_csv(output_combined_matrix_filepath, index=False)
+    return weighted_matrix
+
+
+async def _combine_z_distribution_for_source(
+    merged_source_data: pd.DataFrame,
+    context_name: str,
+    num_replicates: int,
+    output_combined_matrix_filepath: Path,
+    output_figure_filepath: Path,
+    weighted_z_floor: int = -6,
+    weighted_z_ceiling: int = 6,
 ):
-    fig_path = wd / context / "figures"
-    if not fig_path.exists():
-        fig_path.mkdir(parents=True)
-    plot_name_png = fig_path / f"plot_{context}_combine_omics_distro.png"
-
-    weights = []
-    names = []
-    dfs = []
-    counter = 0
-    if comb_batches_z_trna is not None:
-        counter += 1
-        weights.append(trna_weight)
-        names.append("total")
-        dfs.append(comb_batches_z_trna)
-    if comb_batches_z_mrna is not None:
-        counter += 1
-        weights.append(mrna_weight)
-        names.append("polyA")
-        dfs.append(comb_batches_z_mrna)
-    if comb_batches_z_scrna is not None:
-        counter += 1
-        weights.append(scrna_weight)
-        names.append("singleCell")
-        dfs.append(comb_batches_z_scrna)
-    if comb_batches_z_prot is not None:
-        counter += 1
-        weights.append(proteomics_weight)
-        names.append("proteome")
-        dfs.append(comb_batches_z_prot)
-
-    def weighted_z(x, weights):
-        floor_score = -6
-        ceil_score = 10
-        x = np.array(x, dtype=float)
-        nas = np.where(np.isnan(x))[0]
-        if len(nas) > 0:
-            x = np.delete(x, nas)
-            weights = np.delete(weights, nas)
-        weights = weights / np.sum(weights)
-        numer = np.sum(weights * x)
-        denom = np.sqrt(np.sum(weights**2))
-        result = numer / denom
-        return np.clip(result, floor_score, ceil_score)
-
-    zmat = dfs[0].copy()
-    zmat.columns = ["entrez_gene_id", names[0]]
-    for i in range(1, counter):
-        add_df = dfs[i]
-        add_df.columns = ["entrez_gene_id", names[i]]
-        zmat = pd.merge(zmat, add_df, on="entrez_gene_id", how="outer")
-
-    if zmat.shape[1] > 2:
-        combine_z = np.apply_along_axis(weighted_z, axis=1, arr=zmat.iloc[:, 1:].values, weights=weights)
-    else:
-        combine_z = zmat.iloc[:, 1:].values
-
-    combine_z = combine_z.ravel()
-    merge_df = pd.concat([zmat, pd.Series(combine_z, name="combined")], axis=1)
-    combine_z = pd.DataFrame({"entrez_gene_id": zmat["entrez_gene_id"].astype(str), "combine_z": combine_z})
-
-    stack_df = pd.concat(
-        [
-            pd.DataFrame({"entrez_gene_id": merge_df["entrez_gene_id"], "zscore": merge_df[col].astype(float), "source": col})
-            for col in merge_df.columns[1:]
-        ]
+    if _num_columns(merged_source_data) <= 2:
+        logger.warning("A single source exists, returning matrix as-is because no additional combining can be done")
+        merged_source_data.columns = ["ensembl_gene_id", "combine_z"]
+        return merged_source_data
+
+    output_combined_matrix_filepath.parent.mkdir(parents=True, exist_ok=True)
+    output_figure_filepath.parent.mkdir(parents=True, exist_ok=True)
+
+    logger.trace(f"Found {_num_columns(merged_source_data) - 1} samples for context '{context_name}' to combine")
+    values = merged_source_data.iloc[:, 1:].values
+    mask = ~np.isnan(values)
+    masked_values = np.where(mask, values, 0)  # Replace NaN with 0
+    masked_num_replicates = np.where(mask, num_replicates, 0)
+
+    weights = masked_num_replicates / np.sum(masked_num_replicates, axis=1, keepdims=True)
+    numerator = np.sum(weights * masked_values, axis=1)
+    denominator = np.sqrt(np.sum(weights**2, axis=1))
+    weighted_matrix = numerator / denominator
+    weighted_matrix = np.clip(weighted_matrix, weighted_z_floor, weighted_z_ceiling)
+    logger.trace("Finished combining z-distribution")
+    # merge_df = pd.concat([merged_source_data, pd.Series(weighted_matrix, name="combined")], axis=1)
+    weighted_matrix = pd.DataFrame(
+        {
+            "ensembl_gene_id": merged_source_data["ensembl_gene_id"],
+            "combine_z": weighted_matrix,
+        }
     )
 
-    fig = px.histogram(
-        stack_df,
-        x="zscore",
-        color="source",
-        nbins=100,  # Adjust as needed
-        marginal="rug",
-        title=f"Combined Omics Z-score Distribution for {context}",
+    # stack_df = pd.melt(
+    #     merge_df,
+    #     id_vars=["ensembl_gene_id"],
+    #     value_vars=merge_df.columns[1:],  # all other columns are values
+    #     var_name="source",
+    #     value_name="zscore",
+    # )
+    # graph_zscore_distribution(
+    #     df=stack_df,
+    #     title=f"Combined Z-score Distribution for {context_name}",
+    #     output_filepath=output_figure_filepath,
+    # )
+    return weighted_matrix
+
+
+def _combine_z_distribution_for_context(
+    context: str,
+    zscore_results: list[_CombineOmicsInput],
+    output_graph_filepath: Path,
+    weighted_z_floor: int = -6,
+    weighted_z_ceiling: int = 6,
+):
+    if not zscore_results:
+        logger.warning("No zscore results exist, returning empty dataframe")
+        return pd.DataFrame({"ensembl_gene_id": [], "combine_z": []})
+
+    z_matrices = [
+        res.z_score_matrix.set_index("ensembl_gene_id").rename(columns=dict.fromkeys([res.type.value for res in zscore_results]))
+        for res in zscore_results
+    ]
+    z_matrix = pd.concat(z_matrices, axis=1, join="outer").reset_index()
+    if _num_columns(z_matrix) <= 1:
+        logger.trace(f"Only 1 source exists for '{context}', returning dataframe as-is becuase no data exists to combine")
+        z_matrix.columns = ["ensembl_gene_id", "combine_z"]
+        return z_matrix
+
+    values = z_matrix.iloc[:, 1:].values
+    weights = np.array([r.weight for r in zscore_results])
+    mask = ~np.isnan(values)
+    masked_values = np.where(mask, values, 0)
+    masked_weights = np.where(mask, weights, 0)
+
+    normalized_weights = masked_weights / np.sum(masked_weights, axis=1, keepdims=True)
+    numerator = np.sum(normalized_weights * masked_values, axis=1)
+    denominator = np.sqrt(np.sum(normalized_weights**2, axis=1))
+    combined_z_matrix = numerator / denominator
+    combined_z_matrix = np.clip(combined_z_matrix, weighted_z_floor, weighted_z_ceiling)
+    combined_z_matrix_df = pd.DataFrame(
+        {
+            "ensembl_gene_id": z_matrix["ensembl_gene_id"],
+            "combine_z": combined_z_matrix,
+        }
     )
 
-    fig.update_layout(xaxis_title="Z-score", yaxis_title="Frequency", font={"family": "sans-serif", "size": 12})
-
-    fig.write_image(plot_name_png)
-
-    return combine_z
-
-
-def _combine_zscores(  # noqa: C901
-    working_dir,
-    context_names,
-    global_use_mrna,
-    global_use_trna,
-    global_use_scrna,
-    global_use_proteins,
-    keep_gene_scores,
-    global_trna_weight,
-    global_mrna_weight,
-    global_scrna_weight,
-    global_protein_weight,
+    stack_df = pd.melt(
+        z_matrix,
+        id_vars=["ensembl_gene_id"],
+        value_vars=z_matrix.columns[1:],
+        var_name="source",
+        value_name="zscore",
+    )
+    combined_df = pd.DataFrame(
+        {
+            "ensembl_gene_id": z_matrix["ensembl_gene_id"],
+            "zscore": combined_z_matrix,
+            "source": "combined",
+        }
+    )
+    stack_df = pd.concat([stack_df, combined_df])
+    # graph_zscore_distribution(
+    #     df=stack_df,
+    #     title=f"Combined Z-score Distribution for {context}",
+    #     output_filepath=output_graph_filepath,
+    # )
+    return combined_z_matrix_df
+
+
+async def _begin_combining_distributions(
+    context_name: str,
+    input_matrices: _InputMatrices,
+    batch_names: _BatchNames,
+    source_weights: _SourceWeights,
+    output_filepaths: _OutputCombinedSourceFilepath,
+    output_figure_dirpath: Path,
+    output_final_model_scores: Path,
+    weighted_z_floor: int = -6,
+    weighted_z_ceiling: int = 6,
 ):
-    working_dir = Path(working_dir)
-    figure_output_dir = working_dir / "figures"
-    figure_output_dir.mkdir(parents=True, exist_ok=True)
-
-    global_trna_batches = _parse_contexts_zfpkm(working_dir, context_names, "total")
-    global_mrna_batches = _parse_contexts_zfpkm(working_dir, context_names, "mrna")
-    global_scrna_batches = _parse_contexts_zumi(working_dir, context_names, "scrna")
-    global_protein_batches = _parse_contexts_zscore_prot(working_dir, context_names)
-
-    for context in context_names:
-        context_use_trna = global_use_trna
-        context_use_mrna = global_use_mrna
-        context_use_scrna = global_use_scrna
-        context_use_proteins = global_use_proteins
+    logger.info(f"Starting to combine z-scores for context '{context_name}'")
+    output_figure_dirpath.mkdir(parents=True, exist_ok=True)
 
-        context_trna_batch = global_trna_batches.get(context, [])
-        if len(context_trna_batch) == 0 and global_use_trna:
-            context_use_trna = False
-            logger.warning(f"No total RNA-seq zFPKM Matrix files found for {context}. Will not use for this context.")
-
-        context_mrna_batch = global_mrna_batches.get(context, [])
-        if len(context_mrna_batch) == 0 and global_use_mrna:
-            context_use_mrna = False
-            logger.warning(f"No polyA RNA-seq zFPKM Matrix files found for {context}. Will not use for this context.")
-
-        context_scrna_batch = global_scrna_batches.get(context, [])
-        if len(context_scrna_batch) == 0 and global_use_scrna:
-            context_use_scrna = False
-            logger.warning(f"No SC RNA-seq zFPKM Matrix files found for {context}. Will not use for this context.")
-
-        context_protein_batch = global_protein_batches.get(context, [])
-        if len(context_protein_batch) == 0 and global_use_proteins:
-            context_use_proteins = False
-            logger.warning(f"No proteomics z-score Matrix files found for {context}. Will not use for this context.")
-
-        comb_batches_z_trna = None
-        if context_use_trna:
-            logger.info("Will merge total RNA-seq distributions")
-            trna_workdir = working_dir / context / "total"
-            num_reps = []
-            merge_z_data = pd.DataFrame()
-
-            for batch in context_trna_batch:
-                res = _merge_batch(trna_workdir, context, batch)
-                z_matrix = res[0]
-                num_reps.extend(res[1])
-                combine_z_matrix = _combine_batch_zdistro(trna_workdir, context, batch, z_matrix)
-                combine_z_matrix.columns = ["entrez_gene_id", batch]
-                merge_z_data = combine_z_matrix if merge_z_data.empty else pd.merge(merge_z_data, combine_z_matrix, on="entrez_gene_id", how="outer")
-
-            comb_batches_z_trna = _combine_context_zdistro(trna_workdir, context, num_reps, merge_z_data)
-            filename = trna_workdir / f"combined_zFPKM_{context}.csv"
-            comb_batches_z_trna.to_csv(filename, index=False)
-
-            if not context_use_proteins and not context_use_mrna and not context_use_scrna:
-                filename = trna_workdir / f"model_scores_{context}.csv"
-                comb_batches_z_trna.to_csv(filename, index=False)
-
-        comb_batches_z_mrna = None
-        if context_use_mrna:
-            logger.info("Will merge polyA enriched RNA-seq distributions")
-            mrna_workdir = working_dir / context / "mrna"
-            num_reps = []
-            merge_z_data = pd.DataFrame()
-            for batch in context_mrna_batch:
-                res = _merge_batch(mrna_workdir, context, batch)
-                z_matrix = res[0]
-                num_reps.extend(res[1])
-                combine_z_matrix = _combine_batch_zdistro(mrna_workdir, context, batch, z_matrix)
-                combine_z_matrix.columns = ["entrez_gene_id", batch]
-                merge_z_data = combine_z_matrix if merge_z_data.empty else pd.merge(merge_z_data, combine_z_matrix, on="entrez_gene_id", how="outer")
-
-            comb_batches_z_mrna = _combine_context_zdistro(mrna_workdir, context, num_reps, merge_z_data)
-            filename = mrna_workdir / f"combined_zFPKM_{context}.csv"
-            comb_batches_z_mrna.to_csv(filename, index=False)
-
-            if not context_use_proteins and not context_use_trna and not context_use_scrna:
-                filename = mrna_workdir / f"model_scores_{context}.csv"
-                comb_batches_z_mrna.to_csv(filename, index=False)
-
-        comb_batches_z_scrna = None
-        if context_use_scrna:
-            logger.info(f"Will merge single-cell RNA-seq distributions for {context}")
-            scrna_workdir = working_dir / context / "scrna"
-            num_reps = []
-            merge_z_data = pd.DataFrame()
-            for batch in context_scrna_batch:
-                res = _merge_batch(scrna_workdir, context, batch)
-                z_matrix = res[0]
-                num_reps.extend(res[1])
-                combine_z_matrix = _combine_batch_zdistro(scrna_workdir, context, batch, z_matrix)
-                combine_z_matrix.columns = ["entrez_gene_id", batch]
-                merge_z_data = combine_z_matrix if merge_z_data.empty else pd.merge(merge_z_data, combine_z_matrix, on="entrez_gene_id", how="outer")
-
-            comb_batches_z_scrna = _combine_context_zdistro(scrna_workdir, context, num_reps, merge_z_data)
-            filename = scrna_workdir / f"combined_zFPKM_{context}.csv"
-            comb_batches_z_scrna.to_csv(filename, index=False)
-
-            if not context_use_proteins and not context_use_trna and not context_use_mrna:
-                filename = scrna_workdir / f"model_scores_{context}.csv"
-                comb_batches_z_scrna.to_csv(filename, index=False)
-
-        comb_batches_z_prot = None
-        if context_use_proteins:
-            logger.info("Will merge protein abundance distributions")
-            protein_workdir = working_dir / context / "proteomics"
-            num_reps = []
-            merge_z_data = pd.DataFrame()
-            for batch in context_protein_batch:
-                res = _merge_batch(protein_workdir, context, batch)
-                z_matrix = res[0]
-                num_reps.extend(res[1])
-                combine_z_matrix = _combine_batch_zdistro(protein_workdir, context, batch, z_matrix)
-                combine_z_matrix.columns = ["entrez_gene_id", batch]
-                merge_z_data = combine_z_matrix if merge_z_data.empty else pd.merge(merge_z_data, combine_z_matrix, on="entrez_gene_id", how="outer")
-
-            comb_batches_z_prot = _combine_context_zdistro(protein_workdir, context, num_reps, merge_z_data)
-            filename = protein_workdir / f"combined_zscore_proteinAbundance_{context}.csv"
-            comb_batches_z_prot.to_csv(filename, index=False)
-
-            if not context_use_mrna and not context_use_trna and not context_use_scrna:
-                filename = protein_workdir / f"model_scores_{context}.csv"
-                comb_batches_z_prot.to_csv(filename, index=False)
-
-        if comb_batches_z_mrna is None and comb_batches_z_trna is None and comb_batches_z_scrna is None and comb_batches_z_prot is None:
-            logger.critical(f"The context '{context}' was found in the configuration file but no data was found on disk!")
+    z_score_results: list[_CombineOmicsInput] = []
+    for source, matrix in input_matrices:
+        if matrix is None:
+            logger.trace(f"Source '{source.value}' is None, skipping")
             continue
-
-        comb_omics_z = _combine_omics_zdistros(
-            wd=working_dir,
-            context=context,
-            comb_batches_z_trna=comb_batches_z_trna,
-            comb_batches_z_mrna=comb_batches_z_mrna,
-            comb_batches_z_scrna=comb_batches_z_scrna,
-            comb_batches_z_prot=comb_batches_z_prot,
-            trna_weight=global_trna_weight,
-            mrna_weight=global_mrna_weight,
-            scrna_weight=global_scrna_weight,
-            proteomics_weight=global_protein_weight,
+        if source not in SourceTypes:
+            logger.critical(f"Invalid source; got '{source.value}', expected 'trna', 'mrna', 'scrna', or 'proteomics'.")
+            raise ValueError("Invalid source")
+
+        batch_results = await asyncio.gather(
+            *[
+                _combine_z_distribution_for_batch(
+                    context_name=context_name,
+                    batch=batch,
+                    matrix=matrix[[GeneIdentifier.ENSEMBL_GENE_ID.value, *batch.sample_names]],
+                    source=source,
+                    output_combined_matrix_filepath=(
+                        output_filepaths[source.value].parent / f"{context_name}_{source.value}_batch{batch.batch_num}_combined_z_distribution_.csv"
+                    ),
+                    output_figure_dirpath=output_figure_dirpath,
+                    weighted_z_floor=weighted_z_floor,
+                    weighted_z_ceiling=weighted_z_ceiling,
+                )
+                for batch in batch_names[source.value]
+            ]
         )
 
-        filename = working_dir / context / f"model_scores_{context}.csv"
-        comb_omics_z.to_csv(filename, index=False)
+        merged_batch_results = pd.DataFrame()
+        for df in batch_results:
+            merged_batch_results = df if merged_batch_results.empty else merged_batch_results.merge(df, on="ensembl_gene_id", how="outer")
+
+        merged_source_results: pd.DataFrame = await _combine_z_distribution_for_source(
+            merged_source_data=merged_batch_results,
+            context_name=context_name,
+            num_replicates=sum(batch.num_samples for batch in batch_names[source.value]),
+            output_combined_matrix_filepath=(
+                output_filepaths[source.value].parent / f"{context_name}_{source.value}_combined_zscore_distribution.csv"
+            ),
+            output_figure_filepath=(output_figure_dirpath / f"{context_name}_{source.value}_combined_zscore_distribution.pdf"),
+            weighted_z_floor=weighted_z_floor,
+            weighted_z_ceiling=weighted_z_ceiling,
+        )
+        z_score_results.append(
+            _CombineOmicsInput(
+                z_score_matrix=merged_source_results,
+                type=source,
+                weight=source_weights[source.value],
+            )
+        )
+        merged_source_results.to_csv(output_filepaths[source.value])
+        logger.success(f"Wrote z-scores for source '{source.value}' in context '{context_name}' to '{output_filepaths[source.value]}'")
+
+    logger.trace(f"Combining z-score distributions for all sources in context '{context_name}'")
+    merged_context_results = _combine_z_distribution_for_context(
+        context=context_name,
+        zscore_results=z_score_results,
+        output_graph_filepath=output_figure_dirpath / f"{context_name}_combined_omics_distribution.pdf",
+    )
+    merged_context_results.to_csv(output_final_model_scores, index=False)
+    logger.success(f"Finished combining z-scores for context '{context_name}'")
diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index f6f71fa5..90226076 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -1,14 +1,11 @@
 from __future__ import annotations
 
-import argparse
-import ast
 import collections
 import re
+import sys
 from collections.abc import Sequence
-from dataclasses import dataclass
-from enum import Enum
+from io import TextIOWrapper
 from pathlib import Path
-from typing import NamedTuple
 
 import cobra
 import numpy as np
@@ -22,89 +19,8 @@
 from troppo.methods.reconstruction.imat import IMAT, IMATProperties
 from troppo.methods.reconstruction.tINIT import tINIT, tINITProperties
 
-from como.project import Config
-from como.utils import Algorithm, Compartments, split_gene_expression_data, stringlist_to_list
-
-
-class Solver(Enum):
-    """Solver used to seed context specific model."""
-
-    GLPK = "GLPK"
-    GUROBI = "GUROBI"
-    SCIPY = "SCIPY"
-    GLPK_EXACT = "GLPK_EXACT"
-
-    @staticmethod
-    def from_string(value: str) -> Solver:
-        """Convert string to Solver enum."""
-        match value.lower():
-            case "glpk":
-                return Solver.GLPK
-            case "gurobi":
-                return Solver.GUROBI
-            case "scipy":
-                return Solver.SCIPY
-            case "glpk_exact":
-                return Solver.GLPK_EXACT
-            case _:
-                raise ValueError(f"Unknown solver: {value}")
-
-
-class _BoundaryReactions(NamedTuple):
-    """Boundary reactions to be used in the context specific model."""
-
-    reactions: list[str]
-    lower_bounds: list[float]
-    upper_bounds: list[float]
-
-
-class _BuildResults(NamedTuple):
-    """Results of building a context specific model."""
-
-    model: cobra.Model
-    expression_index_list: list[int]
-    infeasible_reactions: pd.DataFrame
-
-
-@dataclass
-class _Arguments:
-    """Arguments for creating a context specific model."""
-
-    context_name: str
-    reference_model: Path
-    active_genes_filepath: Path
-    objective: str
-    boundary_reactions_filepath: Path
-    exclude_reactions_filepath: Path
-    force_reactions_filepath: Path
-    recon_algorithm: Algorithm
-    solver: Solver
-    low_threshold: int
-    high_threshold: int
-    output_filetypes: list[str]
-
-    def __post_init__(self):
-        self.reference_model = Path(self.reference_model)
-        self.active_genes_filepath = Path(self.active_genes_filepath)
-        self.boundary_reactions_filepath = Path(self.boundary_reactions_filepath) if self.boundary_reactions_filepath is not None else None
-        self.exclude_reactions_filepath = Path(self.exclude_reactions_filepath) if self.exclude_reactions_filepath is not None else None
-        self.force_reactions_filepath = Path(self.force_reactions_filepath) if self.force_reactions_filepath is not None else None
-
-        if not self.reference_model.exists():
-            raise FileNotFoundError(f"Reference model not found at {self.reference_model}")
-        if not self.active_genes_filepath.exists():
-            raise FileNotFoundError(f"Active genes file not found at {self.active_genes_filepath}")
-        if self.boundary_reactions_filepath and not self.boundary_reactions_filepath.exists():
-            raise FileNotFoundError(f"Boundary reactions file not found at {self.boundary_reactions_filepath}")
-        if self.exclude_reactions_filepath and not self.exclude_reactions_filepath.exists():
-            raise FileNotFoundError(f"Exclude reactions file not found at {self.exclude_reactions_filepath}")
-        if self.force_reactions_filepath and not self.force_reactions_filepath.exists():
-            raise FileNotFoundError(f"Force reactions file not found at {self.force_reactions_filepath}")
-
-        if self.high_threshold < self.low_threshold:
-            raise ValueError(
-                f"Low threshold must be less than high threshold. Received low threshold: {self.low_threshold}, high threshold: {self.high_threshold}"
-            )
+from como.data_types import Algorithm, CobraCompartments, LogLevel, Solver, _BoundaryReactions, _BuildResults
+from como.utils import _log_and_raise_error, _read_file, _set_up_logging, split_gene_expression_data
 
 
 def _correct_bracket(rule: str, name: str) -> str:
@@ -131,9 +47,9 @@ def _correct_bracket(rule: str, name: str) -> str:
         elif len(left_name) > 0 and char == left_name[0]:
             new_right_rule.append(char)
             left_name = left_name[1:]
-    new_left_rule = "".join(new_right_rule)
+    new_left_rule = "".join(new_right_rule).strip()
     final_right_rule = "" if rule_match is None else _correct_bracket(right_rule, right_name)
-    return " ".join([new_left_rule, operator, final_right_rule])
+    return " ".join([new_left_rule, operator, final_right_rule]).strip()
 
 
 def _gene_rule_logical(gpr_expression: str, level: int = 0) -> str:
@@ -142,10 +58,10 @@ def _gene_rule_logical(gpr_expression: str, level: int = 0) -> str:
         loc_r = gpr_expression.index(")")
     except ValueError:
         if "and" in gpr_expression:
-            gpr_expression = gpr_expression.replace("and", ",")
+            gpr_expression = gpr_expression.replace(" and ", ", ")
             return "min{" + gpr_expression + "}"
         elif "or" in gpr_expression:
-            gpr_expression = gpr_expression.replace("or", ",")
+            gpr_expression = gpr_expression.replace(" or ", ", ")
             return "max{" + gpr_expression + "}"
         else:
             gpr_expression = gpr_expression.replace("[", "")
@@ -171,51 +87,37 @@ def _gene_rule_logical(gpr_expression: str, level: int = 0) -> str:
     return expression_out
 
 
-def _gene_rule_evaluable(expression_in: str) -> str:
-    """Make expression rule evaluable."""
-    gene_reaction_by_rule = _gene_rule_logical(expression_in)
-    gene_reaction_by_rule = gene_reaction_by_rule.replace("{", "(")
-    gene_reaction_by_rule = gene_reaction_by_rule.replace("}", ")")
-
-    return gene_reaction_by_rule
-
-
-def _set_boundaries(model: cobra.Model, bound_rxns: list, bound_lb, bound_ub) -> tuple[cobra.Model, list]:
-    all_rxns = model.reactions  # get all reactions
-    bound_rm_rxns = []
-
+def _set_boundaries(
+    model: cobra.Model,
+    boundary_reactions: list[str],
+    lower_bounds: list[float],
+    upper_bounds: list[float],
+) -> cobra.Model:
     # get boundary reactions
-    exchange_rxns = [rxn.id for rxn in all_rxns if re.search("EX_", rxn.id)]
-    sink_rxns = [rxn.id for rxn in all_rxns if re.search("sink_", rxn.id)]
-    demand_rxns = [rxn.id for rxn in all_rxns if re.search("DM_", rxn.id)]
+    exchange_rxns = [rxn.id for rxn in model.reactions if re.search("EX_", rxn.id)]
+    sink_rxns = [rxn.id for rxn in model.reactions if re.search("sink_", rxn.id)]
+    demand_rxns = [rxn.id for rxn in model.reactions if re.search("DM_", rxn.id)]
 
     # Allows all boundary reactions to be used if none are given
-    allow_all_boundary_rxns = not bound_rxns
+    allow_all_boundary_rxns = not boundary_reactions
 
     # close sinks and demands not in boundary reactions unless no boundary reactions were given
     if not allow_all_boundary_rxns:
         for i, rxn in enumerate(sink_rxns):  # set sinks to 0
-            if rxn not in bound_rxns:  # only allow sink accumulation
-                getattr(model.reactions, rxn).lower_bounds = 0
-                getattr(model.reactions, rxn).upper_bounds = 1000
-            else:  # set from file
-                getattr(model.reactions, rxn).lower_bounds = bound_lb[i]
-                getattr(model.reactions, rxn).upper_bounds = bound_ub[i]
+            getattr(model.reactions, rxn).lower_bounds = lower_bounds[i] if rxn in boundary_reactions else 0
+            getattr(model.reactions, rxn).upper_bounds = upper_bounds[i] if rxn in boundary_reactions else 1000
 
         for i, rxn in enumerate(demand_rxns):
             getattr(model.reactions, rxn).lower_bounds = 0
-            getattr(model.reactions, rxn).upper_bounds = bound_ub[i] if rxn in bound_rxns else 0
+            getattr(model.reactions, rxn).upper_bounds = upper_bounds[i] if rxn in boundary_reactions else 0
 
     # Reaction media
-    medium = model.medium  # get reaction media to modify
+    medium = model.medium
     for rxn in exchange_rxns:  # open exchanges from exchange file, close unspecified exchanges
-        if rxn not in bound_rxns:
-            medium[rxn] = 0.0
-        else:
-            medium[rxn] = -float(bound_lb[bound_rxns.index(rxn)])
-    model.medium = medium  # set new media
+        medium[rxn] = -float(lower_bounds[boundary_reactions.index(rxn)]) if rxn in boundary_reactions else 0.0
+    model.medium = medium
 
-    return model, bound_rm_rxns
+    return model
 
 
 def _feasibility_test(model_cobra: cobra.Model, step: str):
@@ -246,7 +148,7 @@ def _feasibility_test(model_cobra: cobra.Model, step: str):
     return incon_rxns, model_cobra_rm
 
 
-def _build_with_gimme(cobra_model, s_matrix, lb, ub, idx_objective, expr_vector):
+def _build_with_gimme(cobra_model, s_matrix, lower_bounds, upper_bounds, idx_objective, expr_vector):
     # `Becker and Palsson (2008). Context-specific metabolic networks are
     # consistent with experiments. PLoS Comput. Biol. 4, e1000082.`
     properties = GIMMEProperties(
@@ -256,23 +158,23 @@ def _build_with_gimme(cobra_model, s_matrix, lb, ub, idx_objective, expr_vector)
         preprocess=True,
         flux_threshold=0.9,
     )
-    algorithm = GIMME(s_matrix, lb, ub, properties)
+    algorithm = GIMME(s_matrix, lower_bounds, upper_bounds, properties)
     gene_activity = algorithm.run()
     context_cobra_model = cobra_model.copy()
-    r_ids = [r.id for r in context_cobra_model.reactions]
-    to_remove_ids = [r_ids[r] for r in np.where(gene_activity == 0)[0]]
+    reaction_ids = [r.id for r in context_cobra_model.reactions]
+    to_remove_ids = [reaction_ids[r] for r in np.where(gene_activity == 0)[0]]
 
     context_cobra_model.remove_reactions(to_remove_ids, True)
-    r_ids = [r.id for r in context_cobra_model.reactions]
     psol = pfba(context_cobra_model)  # noqa: F841
+    # reaction_ids = [r.id for r in context_cobra_model.reactions]
     # psol = context_cobra_model.optimize()
-    # to_remove_ids = [r_ids[r] for r in np.where(abs(psol.fluxes) < 1e-8)[0]]
+    # to_remove_ids = [reaction_ids[r] for r in np.where(abs(psol.fluxes) < 1e-8)[0]]
     # context_cobra_model.remove_reactions(to_remove_ids, True)
 
     return context_cobra_model
 
 
-def _build_with_fastcore(cobra_model, s_matrix, lb, ub, exp_idx_list, solver):
+def _build_with_fastcore(cobra_model, s_matrix, lower_bounds, upper_bounds, exp_idx_list, solver):
     # 'Vlassis, Pacheco, Sauter (2014). Fast reconstruction of compact
     # context-specific metabolic network models. PLoS Comput. Biol. 10,
     # e1003424.'
@@ -280,7 +182,7 @@ def _build_with_fastcore(cobra_model, s_matrix, lb, ub, exp_idx_list, solver):
     logger.debug("Creating feasible model")
     _, cobra_model = _feasibility_test(cobra_model, "other")
     properties = FastcoreProperties(core=exp_idx_list, solver=solver)
-    algorithm = FASTcore(s_matrix, lb, ub, properties)
+    algorithm = FASTcore(s_matrix, lower_bounds, upper_bounds, properties)
     context_rxns = algorithm.fastcore()
     context_cobra_model = cobra_model.copy()
     r_ids = [r.id for r in context_cobra_model.reactions]
@@ -293,22 +195,22 @@ def _build_with_fastcore(cobra_model, s_matrix, lb, ub, exp_idx_list, solver):
 def _build_with_imat(
     cobra_model: cobra.Model,
     s_matrix: npt.NDArray,
-    lb: Sequence[float],
-    ub: Sequence[float],
+    lower_bounds: Sequence[float],
+    upper_bounds: Sequence[float],
     expr_vector: npt.NDArray,
-    expr_thesh: tuple[float, float],
+    expr_thresh: tuple[float, float],
     force_gene_ids: Sequence[int],
     solver: str,
 ) -> (cobra.Model, pd.DataFrame):
     expr_vector = np.array(expr_vector)
     properties = IMATProperties(
         exp_vector=expr_vector,
-        exp_thresholds=expr_thesh,
+        exp_thresholds=expr_thresh,
         core=force_gene_ids,
         epsilon=0.01,
         solver=solver.upper(),
     )
-    algorithm = IMAT(s_matrix, np.array(lb), np.array(ub), properties)
+    algorithm = IMAT(s_matrix, np.array(lower_bounds), np.array(upper_bounds), properties)
     context_rxns: npt.NDArray = algorithm.run()
     fluxes: pd.Series = algorithm.sol.to_series()
     context_cobra_model = cobra_model.copy()
@@ -327,83 +229,97 @@ def _build_with_imat(
     return context_cobra_model, flux_df
 
 
-def _build_with_tinit(cobra_model: cobra.Model, s_matrix, lb, ub, expr_vector, solver, idx_force) -> Model:
+def _build_with_tinit(
+    cobra_model: cobra.Model,
+    s_matrix,
+    lower_bounds,
+    upper_bounds,
+    expr_vector,
+    solver,
+    idx_force,
+) -> Model:
     properties = tINITProperties(
         reactions_scores=expr_vector,
+        solver=solver,
         essential_reactions=idx_force,
         production_weight=0.0,
         allow_excretion=False,
         no_reverse_loops=True,
-        solver=solver,
     )
-    algorithm = tINIT(s_matrix, lb, ub, properties)
+    algorithm = tINIT(s_matrix, lower_bounds, upper_bounds, properties)
     algorithm.preprocessing()
     algorithm.build_problem()
-    raise NotImplementedError("tINIT is not yet implemented")
+    _log_and_raise_error("tINIT is not yet implemented.", error=NotImplementedError, level=LogLevel.CRITICAL)
 
 
-def _map_expression_to_reaction(
-    model_cobra,
+async def _map_expression_to_reaction(
+    reference_model,
     gene_expression_file,
     recon_algorithm: Algorithm,
-    low_thresh=None,
-    high_thresh=None,
-):
+    low_thresh: float,
+    high_thresh: float,
+) -> collections.OrderedDict[str, int]:
     """Map gene ids to a reaction based on GPR (gene to protein to reaction) association rules.
 
     These rules should be defined in the general genome-scale metabolic model
     """
-    expression_data = pd.read_csv(gene_expression_file)
-    gene_expressions = split_gene_expression_data(expression_data, recon_algorithm=recon_algorithm)
-    expression_rxns = collections.OrderedDict()
-
-    unknown_val = 1
-    if recon_algorithm in {Algorithm.IMAT, Algorithm.TINIT}:
-        unknown_val = np.mean([low_thresh, high_thresh])  # put unknowns in mid bin
-    elif recon_algorithm == Algorithm.GIMME:
-        unknown_val = -1
-    elif recon_algorithm == Algorithm.FASTCORE:
-        unknown_val = 0
+    gene_activity = split_gene_expression_data(await _read_file(gene_expression_file), recon_algorithm=recon_algorithm)
+    reaction_expression = collections.OrderedDict()
+
+    # fmt: off
+    # Define a default expression value if a gene ID is not found
+    default_expression = (
+        np.mean([low_thresh, high_thresh]) if recon_algorithm in {Algorithm.IMAT, Algorithm.TINIT}
+        else -1 if recon_algorithm in {Algorithm.GIMME}
+        else 0 if recon_algorithm in {Algorithm.FASTCORE}
+        else 1
+    )
+    # fmt: on
 
     error_count = 0
-    for rxn in model_cobra.reactions:
-        gene_reaction_rule = _correct_bracket(rxn.gene_reaction_rule, rxn.gene_name_reaction_rule)
-        gene_ids = re.findall(r"\d+", gene_reaction_rule)
-        expression_rxns[rxn.id] = unknown_val
-        if gene_reaction_rule.strip() == "":
+    for rxn in reference_model.reactions:
+        rxn: cobra.Reaction
+
+        gene_reaction_rule = rxn.gene_reaction_rule
+        if gene_reaction_rule == "":
             continue
-        for gid in gene_ids:
-            rep_val = f" {gene_expressions.at[gid, 'active']} " if gid in gene_expressions.index else f" {unknown_val!s} "
-            gene_reaction_rule = f" {gene_reaction_rule} "  # pad white space to prevent gene matches inside floats
-            gene_reaction_rule = gene_reaction_rule.replace(f" {gid} ", rep_val, 1)
-        try:
-            gene_reaction_by_rule = _gene_rule_evaluable(gene_reaction_rule)
-            gene_reaction_by_rule = gene_reaction_by_rule.strip()
-            expression_rxns[rxn.id] = ast.literal_eval(gene_reaction_by_rule)
 
-        except BaseException:
+        gene_ids = set(re.findall(r"\d+", gene_reaction_rule))
+        reaction_expression[rxn.id] = default_expression
+        for gene_id in gene_ids:
+            activity = f"{gene_activity.at[gene_id, 'active']}" if gene_id in gene_activity.index else f"{default_expression!s}"
+            # replace gene_id with activity, using optional whitespace before and after the gene id
+            # Do not replace the whitespace (if it exists) before and after the gene ID
+            gene_reaction_rule = re.sub(pattern=rf"\b{gene_id}\b", repl=activity, string=gene_reaction_rule)
+
+        try:
+            # We are using eval here because ast.literal_eval is unable to process an evaluable such as `max(-4, -4)`
+            # This isn't ideal, but ultimately the only other option is writing and maintaining a custom parsing engine, which is too much work
+            evaluable_gene_rule = _gene_rule_logical(gene_reaction_rule).replace("{", "(").replace("}", ")")
+            reaction_expression[rxn.id] = eval(evaluable_gene_rule)  # noqa: S307
+        except ValueError:
             error_count += 1
 
-    logger.info(f"Mapped gene expression to reactions, found {error_count} error(s).")
-    expr_vector = np.array(list(expression_rxns.values()), dtype=float)
+    logger.debug(f"Mapped gene expression to reactions, found {error_count} error(s).")
+    # expr_vector = np.array(list(reaction_expression.values()), dtype=float)
 
-    return expression_rxns, expr_vector
+    return reaction_expression
 
 
-def _build_model(  # noqa: C901
-    context_name: str,
+async def _build_model(  # noqa: C901
     general_model_file: Path,
     gene_expression_file: Path,
     recon_algorithm: Algorithm,
     objective: str,
-    bound_rxns: list[str],
-    exclude_rxns: list[str],
-    force_rxns: list[str],
-    bound_lb: list[float],
-    bound_ub: list[float],
+    boundary_reactions: list[str],
+    exclude_reactions: list[str],
+    force_reactions: list[str],
+    lower_bounds: list[float],
+    upper_bounds: list[float],
     solver: str,
     low_thresh: float,
     high_thresh: float,
+    output_flux_result_filepath: Path,
 ) -> _BuildResults:
     """Seed a context specific reference_model.
 
@@ -412,7 +328,6 @@ def _build_model(  # noqa: C901
     file. Metabolite exchange (media), sinks, and demands are determined from exchanges file. Reactions can also be
     force excluded even if they meet GPR association requirements using the force exclude file.
     """
-    config = Config()
     reference_model: cobra.Model
     match general_model_file.suffix:
         case ".mat":
@@ -422,185 +337,229 @@ def _build_model(  # noqa: C901
         case ".json":
             reference_model = cobra.io.load_json_model(general_model_file)
         case _:
-            raise NameError(f"Reference reference_model format must be .xml, .mat, or .json, found '{general_model_file.suffix}'")
-
-    reference_model.objective = {getattr(reference_model.reactions, objective): 1}  # set objective
-
-    if objective not in force_rxns:
-        force_rxns.append(objective)
-
-    # set boundaries
-    reference_model, _ = _set_boundaries(reference_model, bound_rxns, bound_lb, bound_ub)
+            _log_and_raise_error(
+                f"Reference model format must be .xml, .mat, or .json; found '{general_model_file.suffix}'",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
-    # set solver
+    if objective not in force_reactions:
+        force_reactions.append(objective)
+    reference_model = _set_boundaries(reference_model, boundary_reactions, lower_bounds, upper_bounds)
     reference_model.solver = solver.lower()
 
     # check number of unsolvable reactions for reference model under media assumptions
-    # incon_rxns, cobra_model = _feasibility_test(cobra_model, "before_seeding")
-    incon_rxns = []
-
+    # inconsistent_reactions, cobra_model = _feasibility_test(cobra_model, "before_seeding")
+    inconsistent_reactions = []
     s_matrix = cobra.util.array.create_stoichiometric_matrix(reference_model, array_type="dense")
-    lb = []
-    ub = []
-    rx_names = []
+    lower_bounds = []
+    upper_bounds = []
+    reaction_ids = []
     for reaction in reference_model.reactions:
-        lb.append(reaction.lower_bound)
-        ub.append(reaction.upper_bound)
-        rx_names.append(reaction.id)
+        lower_bounds.append(reaction.lower_bound)
+        upper_bounds.append(reaction.upper_bound)
+        reaction_ids.append(reaction.id)
 
     # get expressed reactions
-    expression_rxns, expr_vector = _map_expression_to_reaction(
+    reaction_expression: collections.OrderedDict[str, int] = await _map_expression_to_reaction(
         reference_model,
         gene_expression_file,
         recon_algorithm,
         high_thresh=high_thresh,
         low_thresh=low_thresh,
     )
+    expression_vector: npt.NDArray[np.int32] = np.array(list(reaction_expression.values()), dtype=np.int32)
 
-    for rxn in force_rxns:
-        if rxn not in rx_names:
+    for rxn in force_reactions:
+        if rxn not in reaction_ids:
             logger.warning(
                 f"The force reaction '{rxn}' was not found in the general reference_model. "
                 f"Check BiGG, or the relevant database for your general reference_model, for synonyms."
             )
 
     # collect list of reactions that are infeasible but active in expression data or user defined
-    infeas_exp_rxns = []
-    infeas_force_rxns = []
-    infeas_exp_cnt = 0
-    infeas_force_cnt = 0
+    infeasible_expression_reactions = []
+    infeasible_force_reactions = []
 
-    for idx, rxn in enumerate(expression_rxns):
+    for i, rxn in enumerate(reaction_expression):
         # log reactions in expressed and force lists that are infeasible that the user may wish to review
-        if rxn in incon_rxns and expr_vector[idx] == 1:
-            infeas_exp_cnt += 1
-            infeas_exp_rxns.append(rxn)
-        if rxn in incon_rxns and rxn in force_rxns:
-            infeas_force_cnt += 1
-            infeas_force_rxns.append(rxn)
-
-        # make changes to expressed reactions base on user defined force/exclude reactions
-        # TODO: if not using bound reactions file, add two sets of exchange reactions to be put in either low or mid bin
-
-        if rxn in force_rxns:
-            expr_vector[idx] = high_thresh + 0.1 if recon_algorithm.value in {"TINIT", "IMAT"} else 1
-        if rxn in incon_rxns or rxn in exclude_rxns:
-            expr_vector[idx] = low_thresh - 0.1 if recon_algorithm.value in {"TINIT", "IMAT"} else 0
-
-    idx_obj = rx_names.index(objective)
-    idx_force = [rx_names.index(rxn) for rxn in force_rxns if rxn in rx_names]
-    exp_idx_list = [i for (i, val) in enumerate(expr_vector) if val > 0]  # type: ignore
-    exp_thresh = (low_thresh, high_thresh)
-
-    if recon_algorithm == Algorithm.GIMME:
-        context_model_cobra = _build_with_gimme(reference_model, s_matrix, lb, ub, idx_obj, expr_vector)
-    elif recon_algorithm == Algorithm.FASTCORE:
-        context_model_cobra = _build_with_fastcore(reference_model, s_matrix, lb, ub, exp_idx_list, solver)
-    elif recon_algorithm == Algorithm.IMAT:
-        context_model_cobra: cobra.Model
-        context_model_cobra, flux_df = _build_with_imat(
-            reference_model,
-            s_matrix,
-            lb,
-            ub,
-            expr_vector,
-            exp_thresh,
-            idx_force,
-            solver=solver,
-        )
-        imat_reactions = flux_df.rxn
-        model_reactions = [reaction.id for reaction in context_model_cobra.reactions]
-        reaction_intersections = set(imat_reactions).intersection(model_reactions)
-        flux_df = flux_df[~flux_df["rxn"].isin(reaction_intersections)]
-        flux_df.to_csv(config.data_dir / "results" / context_name / f"{recon_algorithm.value}_flux.csv")
-    elif recon_algorithm == Algorithm.TINIT:
-        context_model_cobra = _build_with_tinit(reference_model, s_matrix, lb, ub, expr_vector, solver, idx_force)
-
-    incon_rxns_cs = []
-    incon_df = pd.DataFrame({"general_infeasible_rxns": list(incon_rxns)})
-    infeas_exp_df = pd.DataFrame({"expressed_infeasible_rxns": infeas_exp_rxns})
-    infeas_force_df = pd.DataFrame({"infeasible_rxns_in_force_list": infeas_exp_rxns})
-    incon_df_cs = pd.DataFrame({"infeasible_rxns_from_seeding": list(incon_rxns_cs)})
-    infeasible_df = pd.concat([incon_df, infeas_exp_df, infeas_force_df, incon_df_cs], ignore_index=True, axis=1)
-    infeasible_df.columns = ["InfeasRxns", "ExpressedInfeasRxns", "ForceInfeasRxns", "ContextInfeasRxns"]
+        if rxn in inconsistent_reactions and expression_vector[i] == 1:
+            infeasible_expression_reactions.append(rxn)
+        if rxn in inconsistent_reactions and rxn in force_reactions:
+            infeasible_force_reactions.append(rxn)
+
+        if rxn in force_reactions:
+            expression_vector[i] = high_thresh + 0.1 if recon_algorithm in {Algorithm.TINIT, Algorithm.IMAT} else 1
+        if rxn in inconsistent_reactions or rxn in exclude_reactions:
+            expression_vector[i] = low_thresh - 0.1 if recon_algorithm in {Algorithm.TINIT, Algorithm.IMAT} else 0
+
+    objective_index = reaction_ids.index(objective)
+    force_reaction_indices = [reaction_ids.index(rxn) for rxn in force_reactions if rxn in reaction_ids]
+    expression_vector_indices = [i for (i, val) in enumerate(expression_vector) if val > 0]  # type: ignore
+    expression_threshold = (low_thresh, high_thresh)
+
+    match recon_algorithm:
+        case Algorithm.GIMME:
+            context_model_cobra = _build_with_gimme(
+                cobra_model=reference_model,
+                s_matrix=s_matrix,
+                lower_bounds=lower_bounds,
+                upper_bounds=upper_bounds,
+                idx_objective=objective_index,
+                expr_vector=expression_vector,
+            )
+        case Algorithm.FASTCORE:
+            context_model_cobra = _build_with_fastcore(
+                cobra_model=reference_model,
+                s_matrix=s_matrix,
+                lower_bounds=lower_bounds,
+                upper_bounds=upper_bounds,
+                exp_idx_list=expression_vector_indices,
+                solver=solver,
+            )
+        case Algorithm.IMAT:
+            context_model_cobra: cobra.Model
+            context_model_cobra, flux_df = _build_with_imat(
+                cobra_model=reference_model,
+                s_matrix=s_matrix,
+                lower_bounds=lower_bounds,
+                upper_bounds=upper_bounds,
+                expr_vector=expression_vector,
+                expr_thresh=expression_threshold,
+                force_gene_ids=force_reaction_indices,
+                solver=solver,
+            )
+            imat_reactions = flux_df.rxn
+            model_reactions = [reaction.id for reaction in context_model_cobra.reactions]
+            reaction_intersections = set(imat_reactions).intersection(model_reactions)
+            flux_df: pd.DataFrame = flux_df[~flux_df["rxn"].isin(reaction_intersections)]
+            flux_df.dropna(inplace=True)
+            flux_df.to_csv(output_flux_result_filepath)
+        case Algorithm.TINIT:
+            context_model_cobra = _build_with_tinit(
+                cobra_model=reference_model,
+                s_matrix=s_matrix,
+                lower_bounds=lower_bounds,
+                upper_bounds=upper_bounds,
+                expr_vector=expression_vector,
+                solver=solver,
+                idx_force=force_reaction_indices,
+            )
+        case _:
+            _log_and_raise_error(
+                (
+                    f"Reconstruction algorithm must be {Algorithm.GIMME.value}, "
+                    f"{Algorithm.FASTCORE.value}, {Algorithm.IMAT.value}, or {Algorithm.TINIT.value}. "
+                    f"Got: {recon_algorithm.value}"
+                ),
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
+
+    inconsistent_and_infeasible_reactions: pd.DataFrame = pd.concat(
+        [
+            pd.DataFrame({"infeasible_reactions": inconsistent_reactions}),
+            pd.DataFrame({"expressed_infeasible_reactions": infeasible_expression_reactions}),
+            pd.DataFrame({"infeasible_force_reactions": infeasible_force_reactions}),
+            pd.DataFrame({"infeasible_context_reactions": []}),  # Included to maintain legacy support
+        ],
+        ignore_index=True,
+        axis=0,
+    )
 
     return _BuildResults(
         model=context_model_cobra,
-        expression_index_list=exp_idx_list,
-        infeasible_reactions=infeasible_df,
+        expression_index_list=expression_vector_indices,
+        infeasible_reactions=inconsistent_and_infeasible_reactions,
     )
 
 
-def _create_df(path: Path) -> pd.DataFrame:
+async def _create_df(path: Path) -> pd.DataFrame:
     match path.suffix:
-        case ".csv":
-            df = pd.read_csv(path, header=0, sep=",")
-        case ".tsv":
-            df = pd.read_csv(path, header=0, sep="\t")
+        case ".csv" | ".tsv":
+            df = await _read_file(path, header=0, sep="," if path.suffix == ".csv" else "\t")
         case ".xlsx" | ".xls":
-            df = pd.read_excel(path, header=0)
+            df = await _read_file(path, header=0)
         case _:
-            raise FileNotFoundError(f"File not found! Must be a csv, tsv, or Excel file. Searching for: {path}")
+            _log_and_raise_error(
+                f"File not found! Must be a csv, tsv, or Excel file. Searching for: {path}",
+                error=FileNotFoundError,
+                level=LogLevel.ERROR,
+            )
     df.columns = [c.lower() for c in df.columns]
     return df
 
 
-def _collect_boundary_reactions(path: Path) -> _BoundaryReactions:
-    df = _create_df(path)
+async def _collect_boundary_reactions(path: Path) -> _BoundaryReactions:
+    df: pd.DataFrame = await _create_df(path)
     for column in df.columns:
         if column not in [
-            "boundary",
+            "reaction",
             "abbreviation",
             "compartment",
             "minimum reaction rate",
             "maximum reaction rate",
         ]:
-            raise ValueError(
-                f"Boundary reactions file must have columns named 'Reaction', 'Abbreviation', 'Compartment', "
-                f"'Minimum Reaction Rate', and 'Maximum Reaction Rate'. Found: {column}"
+            _log_and_raise_error(
+                (
+                    f"Boundary reactions file must have columns named 'Reaction', 'Abbreviation', 'Compartment', "
+                    f"'Minimum Reaction Rate', and 'Maximum Reaction Rate'. Found: {column}"
+                ),
+                error=ValueError,
+                level=LogLevel.ERROR,
             )
 
     reactions: list[str] = [""] * len(df)
-    boundary_type: list[str] = df["boundary"].tolist()
-    reaction_abbreviation: list[str] = df["abbreviation"].tolist()
-    reaction_compartment: list[str] = df["compartment"].tolist()
-    lower_bound = df["minimum reaction rate"].tolist()
-    upper_bound = df["maximum reaction rate"].tolist()
+    boundary_type: list[str] = df["reaction"].tolist()
+    reaction_abbreviation: list[str] = df["abbreviation"].astype(str).tolist()
+    reaction_compartment: list[str] = df["compartment"].astype(str).tolist()
     boundary_map = {"exchange": "EX", "demand": "DM", "sink": "SK"}
     for i in range(len(boundary_type)):
         boundary: str = boundary_type[i].lower()
         if boundary not in boundary_map:
-            raise ValueError(f"Boundary reaction type must be 'Exchange', 'Demand', or 'Sink'. Found: {boundary[i]}")
+            _log_and_raise_error(
+                f"Boundary reaction type must be 'Exchange', 'Demand', or 'Sink'. Found: {boundary}",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
-        shorthand_compartment = Compartments.get(reaction_compartment[i])
+        shorthand_compartment = CobraCompartments.get_shorthand(reaction_compartment[i])
         reactions[i] = f"{boundary_map.get(boundary)}_{reaction_abbreviation[i]}[{shorthand_compartment}]"
 
     return _BoundaryReactions(
         reactions=reactions,
-        lower_bounds=lower_bound,
-        upper_bounds=upper_bound,
+        lower_bounds=df["minimum reaction rate"].tolist(),
+        upper_bounds=df["maximum reaction rate"].tolist(),
     )
 
 
-def _write_model_to_disk(
-    model: cobra.Model,
-    output_directory: Path,
-    context_name: str,
-    output_filetypes: list[str],
-    algorithm: Algorithm,
-) -> None:
-    if "mat" in output_filetypes:
-        cobra.io.save_matlab_model(model, output_directory / f"{context_name}_SpecificModel_{algorithm.value}.mat")
-    if "xml" in output_filetypes:
-        cobra.io.write_sbml_model(model, output_directory / f"{context_name}_SpecificModel_{algorithm.value}.xml")
-    if "json" in output_filetypes:
-        cobra.io.save_json_model(model, output_directory / f"{context_name}_SpecificModel_{algorithm.value}.json")
-
-
-def create_context_specific_model(  # noqa: C901
+def _write_model_to_disk(context_name: str, model: cobra.Model, output_filepaths: list[Path]) -> None:
+    for path in output_filepaths:
+        path.parent.mkdir(parents=True, exist_ok=True)
+        if path.suffix == ".mat":
+            cobra.io.save_matlab_model(model=model, file_name=path)
+        elif path.suffix == ".json":
+            cobra.io.save_json_model(model=model, filename=path, pretty=True)
+        elif path.suffix in {".sbml", ".xml"}:
+            cobra.io.write_sbml_model(cobra_model=model, filename=path)
+        else:
+            _log_and_raise_error(
+                f"Invalid output model filetype. Should be one of .xml, .sbml, .mat, or .json. Got '{path.suffix}'",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
+        logger.success(f"Saved metabolic model for context '{context_name}' to '{path}'")
+
+
+async def create_context_specific_model(  # noqa: C901
     context_name: str,
     reference_model: Path,
-    genes_file: Path,
+    active_genes_filepath: Path,
+    output_infeasible_reactions_filepath: Path,
+    output_flux_result_filepath: Path,
+    output_model_filepaths: Path | list[Path],
+    output_filetypes: list[str] | None = None,
+    output_fastcore_expression_index_filepath: Path | None = None,
     objective: str = "biomass_reaction",
     boundary_rxns_filepath: str | Path | None = None,
     exclude_rxns_filepath: str | Path | None = None,
@@ -609,228 +568,149 @@ def create_context_specific_model(  # noqa: C901
     low_threshold: float = -5,
     high_threshold: float = -3,
     solver: Solver = Solver.GLPK,
-    output_filetypes: list[str] | None = None,
+    log_level: LogLevel = LogLevel.INFO,
+    log_location: str | TextIOWrapper = sys.stderr,
 ):
     """Create a context-specific model using the provided data."""
+    _set_up_logging(level=log_level, location=log_location)
+    output_model_filepaths = [output_model_filepaths] if isinstance(output_model_filepaths, Path) else output_model_filepaths
+    for path in output_model_filepaths:
+        if path.suffix not in {".mat", ".xml", ".sbml", ".json"}:
+            _log_and_raise_error(
+                f"Invalid output model filetype. Should be one of .xml, .sbml, .mat, or .json. Got '{path.suffix}'",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
+    if len(output_model_filepaths) != len(output_model_filepaths):
+        _log_and_raise_error(
+            "The number of output model filepaths must be the same as the number of output flux result filepaths",
+            error=ValueError,
+            level=LogLevel.ERROR,
+        )
+
     if not reference_model.exists():
-        raise FileNotFoundError(f"Reference model not found at {reference_model}")
-    if not genes_file.exists():
-        raise FileNotFoundError(f"Active genes file not found at {genes_file}")
-    if output_filetypes is None:
-        output_filetypes = ["mat"]
+        _log_and_raise_error(
+            f"Reference model not found at {reference_model}",
+            error=FileNotFoundError,
+            level=LogLevel.ERROR,
+        )
+    if not active_genes_filepath.exists():
+        _log_and_raise_error(
+            f"Active genes file not found at {active_genes_filepath}",
+            error=FileNotFoundError,
+            level=LogLevel.ERROR,
+        )
+    if algorithm == Algorithm.FASTCORE and not output_fastcore_expression_index_filepath:
+        _log_and_raise_error(
+            "The fastcore expression index output filepath must be provided",
+            error=ValueError,
+            level=LogLevel.ERROR,
+        )
+    if boundary_rxns_filepath and not boundary_rxns_filepath.exists():
+        _log_and_raise_error(
+            f"Boundary reactions file not found at {boundary_rxns_filepath}",
+            error=FileNotFoundError,
+            level=LogLevel.ERROR,
+        )
 
+    output_filetypes = ["mat"] if output_filetypes is None else output_filetypes
     for output_type in output_filetypes:
         if output_type not in {"xml", "mat", "json"}:
-            raise ValueError(f"Output file type {output_type} not recognized. Must be one of: 'xml', 'mat', 'json'")
+            _log_and_raise_error(
+                f"Output file type {output_type} not recognized. Must be one of: 'xml', 'mat', 'json'",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
     if algorithm not in Algorithm:
-        raise ValueError(f"Algorithm {algorithm} not supported. Use one of {', '.join(a.value for a in Algorithm)}")
+        _log_and_raise_error(
+            f"Algorithm {algorithm} not supported. Use one of {', '.join(a.value for a in Algorithm)}",
+            error=ValueError,
+            level=LogLevel.ERROR,
+        )
 
     if solver not in Solver:
-        raise ValueError(f"Solver '{solver}' not supported. Use one of {', '.join(s.value for s in Solver)}")
+        _log_and_raise_error(
+            f"Solver '{solver}' not supported. Use one of {', '.join(s.value for s in Solver)}",
+            error=ValueError,
+            level=LogLevel.ERROR,
+        )
 
     if boundary_rxns_filepath:
-        boundary_reactions = _collect_boundary_reactions(boundary_rxns_filepath)
+        boundary_reactions = await _collect_boundary_reactions(boundary_rxns_filepath)
 
     exclude_rxns: list[str] = []
     if exclude_rxns_filepath:
         exclude_rxns_filepath: Path = Path(exclude_rxns_filepath)
-        df = _create_df(exclude_rxns_filepath)
+        df = await _create_df(exclude_rxns_filepath)
         if "abbreviation" not in df.columns:
-            raise ValueError("The exclude reactions file should have a single column with a header named Abbreviation")
+            _log_and_raise_error(
+                "The exclude reactions file should have a single column with a header named Abbreviation",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
         exclude_rxns = df["abbreviation"].tolist()
 
     force_rxns: list[str] = []
     if force_rxns_filepath:
         force_rxns_filepath: Path = Path(force_rxns_filepath)
-        df = _create_df(force_rxns_filepath)
+        df = await _create_df(force_rxns_filepath)
         if "abbreviation" not in df.columns:
-            raise ValueError("The force reactions file should have a single column with a header named Abbreviation")
+            _log_and_raise_error(
+                "The force reactions file should have a single column with a header named Abbreviation",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
         force_rxns = df["abbreviation"].tolist()
 
+    # Test that gurobi is using a valid license file
+    if solver == Solver.GUROBI:
+        # test if gurobi is available
+        try:
+            import gurobipy as gp
+        except ImportError as e:
+            logger.error(
+                "The gurobi solver requires the gurobipy package to be installed. "
+                "Please install gurobipy and try again. "
+                "This can be done by installing the 'gurobi' optional dependency."
+            )
+            raise ImportError from e
+
+        env = gp.Env()
+        if env.getParam("WLSACCESSID") == "" or env.getParam("WLSSECRET") == "":
+            logger.critical(
+                "You have requested to use the Gurobi solver but license information cannot be found. "
+                "COMO will continue, but it is HIGHLY unlikely the resulting model will be valid."
+            )
+        # remove gurobi-related information, it is no longer required
+        del env, gp
+
     logger.info(f"Creating '{context_name}' model using '{algorithm.value}' reconstruction and '{solver.value}' solver")
-    build_results: _BuildResults = _build_model(
-        context_name=context_name,
+    build_results: _BuildResults = await _build_model(
         general_model_file=reference_model,
-        gene_expression_file=genes_file,
+        gene_expression_file=active_genes_filepath,
         recon_algorithm=algorithm,
         objective=objective,
-        bound_rxns=boundary_reactions.reactions,
-        bound_lb=boundary_reactions.lower_bounds,
-        bound_ub=boundary_reactions.upper_bounds,
-        exclude_rxns=exclude_rxns,
-        force_rxns=force_rxns,
+        boundary_reactions=boundary_reactions.reactions,
+        exclude_reactions=exclude_rxns,
+        force_reactions=force_rxns,
+        lower_bounds=boundary_reactions.lower_bounds,
+        upper_bounds=boundary_reactions.upper_bounds,
         solver=solver.value.lower(),
         low_thresh=low_threshold,
         high_thresh=high_threshold,
+        output_flux_result_filepath=output_flux_result_filepath,
     )
 
-    config = Config()
-    build_results.infeasible_reactions.to_csv(config.result_dir / context_name / f"{context_name}_infeasible_rxns.csv", index=False)
+    build_results.infeasible_reactions.dropna(inplace=True)
+    build_results.infeasible_reactions.to_csv(output_infeasible_reactions_filepath, index=False)
 
     if algorithm == Algorithm.FASTCORE:
-        pd.DataFrame(build_results.expression_index_list).to_csv(config.result_dir / context_name / f"{context_name}_core_rxns.csv", index=False)
-
-    output_directory = config.result_dir / context_name
-    _write_model_to_disk(
-        model=build_results.model,
-        output_directory=output_directory,
-        context_name=context_name,
-        output_filetypes=output_filetypes,
-        algorithm=algorithm,
-    )
-
-    logger.success(f"Saved output file to {output_directory}")
-    logger.info(f"Number of Genes: {len(build_results.model.genes):,}")
-    logger.info(f"Number of Metabolites: {len(build_results.model.metabolites):,}")
-    logger.info(f"Number of Reactions: {len(build_results.model.reactions):,}")
-
-
-def _parse_args():
-    parser = argparse.ArgumentParser(
-        prog="create_context_specific_model.py",
-        description="Seed a context-specific model from a list of expressed genes, a reference",
-        epilog="For additional help, please post questions/issues in the MADRID GitHub repo at "
-        "https://github.com/HelikarLab/MADRID or email babessell@gmail.com",
-    )
-    parser.add_argument(
-        "-n",
-        "--context-name",
-        type=str,
-        required=True,
-        dest="context_name",
-        help="Name of context or context used consistent with outputs of merge_xomics.py.",
-    )
-    parser.add_argument(
-        "-m",
-        "--reference-model-filepath",
-        type=str,
-        required=True,
-        dest="reference_model",
-        help="Name of Genome-scale metabolic model to seed the context model to. For example, the "
-        "GeneralModelUpdatedV2.mat, is a modified Recon3D model. We also provide iMM_madrid for mouse."
-        "OT can be .mat, .xml, or .json.",
-    )
-    parser.add_argument(
-        "-g",
-        "--active-genes-filepath",
-        type=str,
-        required=True,
-        dest="active_genes_filepath",
-        help="Path to logical table of active genes output from merge_xomics.py called "
-        "ActiveGenes_contextName_Merged.csv. Should be in the corresponding context/context folder "
-        "inside /main/data/results/contextName/. The json file output from the function using "
-        "the context of interest as the key can be used here.",
-    )
-    parser.add_argument(
-        "-o",
-        "--objective",
-        type=str,
-        default="biomass_reaction",
-        dest="objective",
-        help="Reaction ID of the objective function in the model. Generally a biomass function.",
-    )
-    parser.add_argument(
-        "-b",
-        "--boundary-reactions-filepath",
-        type=str,
-        default=None,
-        dest="boundary_reactions_filepath",
-        help="Path to file contains the exchange (media), sink, and demand reactions which "
-        "the model should use to fulfill the reactions governed by transcriptomic and proteomics "
-        "data inputs. It must be a csv or xlsx with three columns: Rxn, Lowerbound, Upperbound. If not "
-        "specified, MADRID will allow ALL BOUNDARY REACTIONS THAT ARE OPEN IN THE REFERENCE MODEL "
-        "TO BE USED!",
-    )
-    parser.add_argument(
-        "-x",
-        "--exclude-reactions-filepath",
-        type=str,
-        default=None,
-        dest="exclude_reactions_filepath",
-        help="Filepath to file that contains reactions which will be removed from active reactions "
-        "the model to use when seeding, even if considered active from transcriptomic and "
-        "proteomics data inputs. It must be a csv or xlsx with one column of reaction IDs consistent with "
-        "the reference model",
-    )
-    parser.add_argument(
-        "-f",
-        "--force-reactions-filepath",
-        type=str,
-        default=None,
-        dest="force_reactions_filepath",
-        help="Filepath to file that contains reactions which will be added to active reactions for "
-        "the model to use when seeding (unless it causes the model to be unsolvable), regardless "
-        "of results of transcriptomic and proteomics data inputs. It must be a csv or xlsx with one "
-        "column of reaction IDs consistent with the reference model",
-    )
-    parser.add_argument(
-        "-a",
-        "--algorithm",
-        type=str,
-        default="GIMME",
-        dest="recon_algorithm",
-        help="Algorithm used to seed context specific model to the Genome-scale model. Can be either GIMME, FASTCORE, iMAT, or tINIT.",
-    )
-    parser.add_argument(
-        "-lt",
-        "--low-threshold",
-        type=float,
-        default=-5,
-        dest="low_threshold",
-        help="Low to mid bin cutoff for iMAT solution",
-    )
-    parser.add_argument(
-        "-ht",
-        "--high-threshold",
-        type=float,
-        default=-3,
-        dest="high_threshold",
-        help="Mid to high bin cutoff for iMAT solution",
-    )
-    parser.add_argument(
-        "-s",
-        "--solver",
-        type=str,
-        default="glpk",
-        dest="solver",
-        help="Solver used to seed model and attempt to solve objective. Default is GLPK, also takes "
-        "GUROBI but you must mount a container license to the Docker to use. An academic license "
-        "can be obtained for free. See the README on the Github or Dockerhub for information on "
-        "mounting this license.",
-    )
-    parser.add_argument(
-        "-t",
-        "--output-filetypes",
-        type=str,
-        nargs="+",
-        default="mat",
-        dest="output_filetypes",
-        help="Filetypes to save seeded model type. Can be either a string with one filetype such as "
-        "'xml' or multiple in the format \"['extension1', 'extension2', ... etc]\". If you want "
-        "to output in all 3 accepted formats,  would be: \"['mat', 'xml', 'json']\" "
-        "Note the outer quotes required to be interpreted by cmd. This a string, not a python list",
-    )
-    args = parser.parse_args()
-    args.output_filetypes = stringlist_to_list(args.output_filetypes)
-    args.solver = Solver.from_string(args.solver)  # type: ignore
-    args.recon_algorithm = Algorithm.from_string(args.recon_algorithm)  # type: ignore
-    return _Arguments(**vars(args))
-
-
-if __name__ == "__main__":
-    args = _parse_args()
-    create_context_specific_model(
-        context_name=args.context_name,
-        reference_model=args.reference_model,
-        genes_file=args.active_genes_filepath,
-        objective=args.objective,
-        boundary_rxns_filepath=args.boundary_reactions_filepath,
-        exclude_rxns_filepath=args.exclude_reactions_filepath,
-        force_rxns_filepath=args.force_reactions_filepath,
-        algorithm=args.recon_algorithm,
-        low_threshold=args.low_threshold,
-        high_threshold=args.high_threshold,
-        solver=args.solver,
-        output_filetypes=args.output_filetypes,
-    )
+        fastcore_df = pd.DataFrame(build_results.expression_index_list)
+        fastcore_df.dropna(inplace=True)
+        fastcore_df.to_csv(output_fastcore_expression_index_filepath, index=False)
+
+    _write_model_to_disk(context_name=context_name, model=build_results.model, output_filepaths=output_model_filepaths)
+    logger.debug(f"Number of Genes: {len(build_results.model.genes):,}")
+    logger.debug(f"Number of Metabolites: {len(build_results.model.metabolites):,}")
+    logger.debug(f"Number of Reactions: {len(build_results.model.reactions):,}")
diff --git a/main/como/data_types.py b/main/como/data_types.py
new file mode 100644
index 00000000..420471a6
--- /dev/null
+++ b/main/como/data_types.py
@@ -0,0 +1,257 @@
+from __future__ import annotations
+
+from collections.abc import Iterator
+from dataclasses import dataclass, field, fields
+from enum import Enum
+from pathlib import Path
+from typing import ClassVar, NamedTuple
+
+import cobra
+import pandas as pd
+from loguru import logger
+
+PATH_TYPE = str | Path
+LOG_FORMAT = "<green>{time:YYYY-MM-DD HH:mm:ss}</> | <level>{level:<8}</> | <cyan>{name}</>:<cyan>{line}</> - <level>{message}</>"
+
+
+class AdjustmentMethod(Enum):
+    """Adjustment method for expression requirement based on differences in number of provided data source types."""
+
+    PROGRESSIVE = "progressive"
+    REGRESSIVE = "regressive"
+    FLAT = "flat"
+    CUSTOM = "custom"
+
+
+class Algorithm(Enum):
+    GIMME = "GIMME"
+    FASTCORE = "FASTCORE"
+    IMAT = "IMAT"
+    TINIT = "TINIT"
+
+
+class FilteringTechnique(Enum):
+    """RNA sequencing filtering capabilities."""
+
+    CPM = "cpm"
+    ZFPKM = "zfpkm"
+    TPM = "tpm"
+    UMI = "umi"
+
+
+class GeneIdentifier(Enum):
+    ENSEMBL_GENE_ID = "ensembl_gene_id"
+    ENTREZ_GENE_ID = "entrez_gene_id"
+    GENE_SYMBOL = "gene_symbol"
+
+
+class LogLevel(Enum):
+    TRACE = 5
+    DEBUG = 10
+    INFO = 20
+    SUCCESS = 25
+    WARNING = 30
+    ERROR = 40
+    CRITICAL = 50
+    NONE = 100
+
+
+class RNAType(Enum):
+    TRNA = "total"
+    MRNA = "mrna"
+    SCRNA = "scrna"
+
+
+class Solver(Enum):
+    """Solver used to seed context specific model."""
+
+    GLPK = "GLPK"
+    GUROBI = "GUROBI"
+    SCIPY = "SCIPY"
+    GLPK_EXACT = "GLPK_EXACT"
+
+
+class SourceTypes(Enum):
+    TRNA = "trna"
+    MRNA = "mrna"
+    SCRNA = "scrna"
+    PROTEOMICS = "proteomics"
+
+
+class PeakIdentificationParameters(NamedTuple):
+    height: float
+    distance: float
+
+
+class CobraCompartments:
+    """Convert from compartment "long-hand" to "short-hand".
+
+    Shorthand from: https://cobrapy.readthedocs.io/en/latest/_modules/cobra/medium/annotations.html
+
+    "Extracellular" -> "e"
+    "golgi" -> "g"
+    """
+
+    SHORTHAND: ClassVar[dict[str, list[str]]] = {
+        "ce": ["cell envelope"],
+        "c": [
+            "cytoplasm",
+            "cytosol",
+            "default",
+            "in",
+            "intra cellular",
+            "intracellular",
+            "intracellular region",
+            "intracellular space",
+        ],
+        "er": ["endoplasmic reticulum"],
+        "erm": ["endoplasmic reticulum membrane"],
+        "e": [
+            "extracellular",
+            "extraorganism",
+            "out",
+            "extracellular space",
+            "extra organism",
+            "extra cellular",
+            "extra-organism",
+            "external",
+            "external medium",
+        ],
+        "f": ["flagellum", "bacterial-type flagellum"],
+        "g": ["golgi", "golgi apparatus"],
+        "gm": ["golgi membrane"],
+        "h": ["chloroplast"],
+        "l": ["lysosome"],
+        "im": ["mitochondrial intermembrane space"],
+        "mm": ["mitochondrial membrane"],
+        "m": ["mitochondrion", "mitochondria"],
+        "n": ["nucleus"],
+        "p": ["periplasm", "periplasmic space"],
+        "x": ["peroxisome", "glyoxysome"],
+        "u": ["thylakoid"],
+        "vm": ["vacuolar membrane"],
+        "v": ["vacuole"],
+        "w": ["cell wall"],
+        "s": ["eyespot", "eyespot apparatus", "stigma"],
+    }
+
+    _REVERSE_LOOKUP: ClassVar[dict[str, list[str]]] = {value.lower(): key for key, values in SHORTHAND.items() for value in values}
+
+    @classmethod
+    def get_shorthand(cls, longhand: str) -> str | None:
+        """Get the short-hand compartment name from the long-hand name."""
+        return cls._REVERSE_LOOKUP.get(longhand.lower(), None)
+
+    @classmethod
+    def get_longhand(cls, shorthand: str) -> str | None:
+        """Get the long-hand compartment name from the short-hand name."""
+        longhand = cls.SHORTHAND.get(shorthand.lower(), None)
+        return longhand[0] if longhand else None
+
+
+class _BuildResults(NamedTuple):
+    """Results of building a context specific model."""
+
+    model: cobra.Model
+    expression_index_list: list[int]
+    infeasible_reactions: pd.DataFrame
+
+
+class _BoundaryReactions(NamedTuple):
+    """Boundary reactions to be used in the context specific model."""
+
+    reactions: list[str]
+    lower_bounds: list[float]
+    upper_bounds: list[float]
+
+
+@dataclass
+class _BatchEntry:
+    batch_num: int
+    sample_names: list[str]
+    _num_samples: int = field(init=False)
+
+    def __post_init__(self):
+        self._num_samples = len(self.sample_names)
+
+    @property
+    def num_samples(self):
+        return self._num_samples
+
+
+@dataclass
+class _CombineOmicsInput:
+    z_score_matrix: pd.DataFrame
+    type: SourceTypes
+    weight: int
+
+
+class _BaseDataType:
+    """Base class for common data types."""
+
+    def __getitem__(self, value: str):
+        """Access matrices using square bracket notation (e.g., `input_matrices['total_rna']`).
+
+        :param value: The name of the matrix to get ('trna', 'mrna', 'scrna', 'proteomics')
+        :returns: The DataFrame if it exists, None otherwise.
+        """
+        self._validate_attribute(value)
+        return getattr(self, value)
+
+    def __setitem__(self, key, value):
+        """Set matrices using square bracket notation (e.g., `input_matrices['total_rna'] = new_df`).
+
+        :param key: The key to set
+        :param value: The new value
+        """
+        self._validate_attribute(key)
+        setattr(self, key, value)
+
+    def _validate_attribute(self, key):
+        if key not in {i.value for i in SourceTypes._member_map_.values()}:
+            # Unable to use como.utils._log_and_raise_error becuase it results in a circular import
+            message = f"{key} is not a valid attribute of {SourceTypes.__name__}; got '{key}'"
+            logger.warning(message)
+            raise ValueError(message)
+
+    def __iter__(self) -> Iterator[tuple[SourceTypes, pd.DataFrame | None]]:
+        """Iterate over matrix fields and their names.
+
+        Yields:
+            A tuple containing (matrix_name, matrix_dataframe).
+
+        """
+        for field_ in fields(self):
+            yield SourceTypes(field_.name), getattr(self, field_.name)
+
+
+@dataclass
+class _BatchNames(_BaseDataType):
+    trna: list[_BatchEntry]
+    mrna: list[_BatchEntry]
+    scrna: list[_BatchEntry]
+    proteomics: list[_BatchEntry]
+
+
+@dataclass
+class _InputMatrices(_BaseDataType):
+    trna: pd.DataFrame | None = None
+    mrna: pd.DataFrame | None = None
+    scrna: pd.DataFrame | None = None
+    proteomics: pd.DataFrame | None = None
+
+
+@dataclass
+class _OutputCombinedSourceFilepath(_BaseDataType):
+    trna: Path | None
+    mrna: Path | None
+    scrna: Path | None
+    proteomics: Path | None
+
+
+@dataclass
+class _SourceWeights(_BaseDataType):
+    trna: int
+    mrna: int
+    scrna: int
+    proteomics: int
diff --git a/main/como/graph/__init__.py b/main/como/graph/__init__.py
new file mode 100644
index 00000000..0822e63a
--- /dev/null
+++ b/main/como/graph/__init__.py
@@ -0,0 +1,47 @@
+from pathlib import Path
+
+import pandas as pd
+import seaborn as sns
+from loguru import logger
+from matplotlib import pyplot as plt
+
+
+def z_score_distribution(
+    df: pd.DataFrame,
+    title: str,
+    output_filepath: Path,
+):
+    """Graph a z-score distribution.
+
+    :param df: The z-score data to graph
+    :param title: Title to add to graph
+    :param output_filepath: Output PNG filepath location
+    :return: None
+    """
+    if output_filepath.suffix not in {".png", ".pdf", ".svg"}:
+        logger.warning(f"Expected .png, .pdf, or .svg suffix for output_png_filepath, got {output_filepath.suffix}. Defaulting to .pdf")
+        output_filepath = output_filepath.with_suffix(".pdf")
+    logger.trace("Graphing z-score distribution")
+    output_filepath.parent.mkdir(parents=True, exist_ok=True)
+    output_filepath.unlink(missing_ok=True)
+
+    plt.figure(figsize=(10, 6))
+
+    if len(df["source"].unique()) == 1:
+        ax = sns.histplot(df, x="zscore", bins=100, kde=True)
+        sns.rugplot(df, x="zscore", ax=ax)
+    else:
+        sns.histplot(df, x="zscore", hue="source", bins=100, kde=True, element="step")
+        plt.legend(loc="upper right", frameon=False, title=None)
+
+    plt.title(title)
+    plt.xlabel("Z-score")
+    plt.ylabel("Frequency")
+    plt.xticks(fontsize=12)
+    plt.yticks(fontsize=12)
+    plt.gca().spines["top"].set_visible(False)
+    plt.gca().spines["right"].set_visible(False)
+    plt.tight_layout()
+    plt.savefig(output_filepath)
+    plt.close()
+    logger.success(f"Saved z-score distribution graph to '{output_filepath}'")
diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index e6c996e2..fda5d9e4 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -1,99 +1,58 @@
 from __future__ import annotations
 
-import argparse
-import json
-import re
+import asyncio
 import sys
-from collections import Counter
-from dataclasses import dataclass
-from enum import Enum
+from io import TextIOWrapper
 from pathlib import Path
 
 import numpy as np
 import pandas as pd
-from fast_bioservices import BioDBNet, Input, Output
+from fast_bioservices.biothings.mygene import MyGene
 from loguru import logger
 
-from como import proteomics_gen, return_placeholder_data
-from como.combine_distributions import _combine_zscores
-from como.custom_types import RNAPrepMethod
+from como.combine_distributions import (
+    _begin_combining_distributions,
+)
+from como.data_types import (
+    AdjustmentMethod,
+    LogLevel,
+    RNAType,
+    SourceTypes,
+    _BatchEntry,
+    _BatchNames,
+    _InputMatrices,
+    _OutputCombinedSourceFilepath,
+    _SourceWeights,
+)
 from como.project import Config
-from como.utils import split_gene_expression_data
+from como.utils import _log_and_raise_error, _read_file, _set_up_logging, get_missing_gene_data, return_placeholder_data
 
 
 class _MergedHeaderNames:
-    PROTEOMICS = "prote"
     TRNASEQ = "trnaseq"
     MRNASEQ = "mrnaseq"
     SCRNASEQ = "scrnaseq"
+    PROTEOMICS = "prote"
 
 
 class _ExpressedHeaderNames:
-    PROTEOMICS = f"{_MergedHeaderNames.PROTEOMICS}_exp"
     TRNASEQ = f"{_MergedHeaderNames.TRNASEQ}_exp"
     MRNASEQ = f"{_MergedHeaderNames.MRNASEQ}_exp"
     SCRNASEQ = f"{_MergedHeaderNames.SCRNASEQ}_exp"
+    PROTEOMICS = f"{_MergedHeaderNames.PROTEOMICS}_exp"
 
 
 class _HighExpressionHeaderNames:
-    PROTEOMICS = f"{_MergedHeaderNames.PROTEOMICS}_high"
     TRNASEQ = f"{_MergedHeaderNames.TRNASEQ}_high"
     MRNASEQ = f"{_MergedHeaderNames.MRNASEQ}_high"
     SCRNASEQ = f"{_MergedHeaderNames.SCRNASEQ}_high"
+    PROTEOMICS = f"{_MergedHeaderNames.PROTEOMICS}_high"
 
 
-class AdjustmentMethod(Enum):
-    """Adjustment method for expression requirement based on differences in number of provided data source types."""
-
-    PROGRESSIVE = "progressive"
-    REGRESSIVE = "regressive"
-    FLAT = "flat"
-    CUSTOM = "custom"
-
-    @classmethod
-    def from_string(cls, value: str) -> AdjustmentMethod:
-        """Convert a string to an AdjustmentMethod enum."""
-        if value.lower() not in [t.value for t in cls]:
-            raise ValueError(f"Adjustment method must be one of {cls}; got: {value}")
-        return cls(value)
-
-
-@dataclass
-class _Arguments:
-    trnaseq_filename: str
-    mrnaseq_filename: str
-    scrnaseq_filename: str
-    proteomics_filename: str
-    expression_requirement: str | int
-    adjustment_method: AdjustmentMethod
-    no_high_confidence: bool
-    no_na: bool
-    merge_zfpkm_distribution: bool
-    keep_transcriptomics_scores: bool
-    trna_weight: int
-    mrna_weight: int
-    scrna_weight: int
-    proteomics_weight: int
-    custom_expression_filename: str
-
-    def __post_init__(self):
-        if self.expression_requirement.isdigit():
-            self.expression_requirement = int(self.expression_requirement)
-            if self.expression_requirement < 1:
-                logger.warning(f"Expression requirement should be at least 1, setting to 1 now. Got {self.expression_requirement}")
-                self.expression_requirement = 1
-        elif self.expression_requirement != "default":
-            raise ValueError(f"Expression requirement should be an integer or 'default', got {self.expression_requirement}")
-
-        if self.adjustment_method.value.lower() not in ["progressive", "regressive", "flat", "custom"]:
-            raise ValueError("Adjust method must be either 'progressive', 'regressive', 'flat', or 'custom'")
-
-
-def _load_rnaseq_tests(filename, context_name, prep_method: RNAPrepMethod) -> tuple[str, pd.DataFrame]:
-    """Load rnaseq results.
-
-    Returns a dictionary of test (context, context, cell, etc ) names and rnaseq expression data
-    """
+# TODO: If function is no longer needed, remove?
+async def _load_rnaseq_tests(filename, context_name, prep_method: RNAType) -> tuple[str, pd.DataFrame]:
+    """Load rnaseq results."""
+    logger.debug(f"Loading data for context '{context_name}' using preparation method '{prep_method.value}'")
     config = Config()
 
     def load_dummy_dict():
@@ -104,22 +63,32 @@ def load_dummy_dict():
         return load_dummy_dict()
 
     inquiry_full_path = config.data_dir / "config_sheets" / filename
-    if not inquiry_full_path.exists():  # check that config file exist (isn't needed to load, but helps user)
-        raise FileNotFoundError(f"Error: Config file not found at {inquiry_full_path}")
+    if not inquiry_full_path.exists():
+        _log_and_raise_error(
+            f"Config file not found at {inquiry_full_path}",
+            error=FileNotFoundError,
+            level=LogLevel.ERROR,
+        )
 
     match prep_method:
-        case RNAPrepMethod.TOTAL:
-            filename = f"rnaseq_total_{context_name}.csv"
-        case RNAPrepMethod.MRNA:
-            filename = f"rnaseq_mrna_{context_name}.csv"
-        case RNAPrepMethod.SCRNA:
-            filename = f"rnaseq_scrna_{context_name}.csv"
+        case RNAType.TRNA:
+            filename = f"{RNAType.TRNA.value}_{context_name}.csv"
+        case RNAType.MRNA:
+            filename = f"{RNAType.MRNA.value}_{context_name}.csv"
+        case RNAType.SCRNA:
+            filename = f"{RNAType.SCRNA.value}_{context_name}.csv"
         case _:
-            raise ValueError(f"Unsupported RNA-seq library type: {prep_method.value}. Must be an option defined in 'RNASeqPreparationMethod'.")
+            _log_and_raise_error(
+                f"Unsupported RNA-seq library type: {prep_method.value}. Must be one of {', '.join(RNAType)}.",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
     save_filepath = config.result_dir / context_name / prep_method.value / filename
     if save_filepath.exists():
+        logger.debug(f"Loading RNA-seq data from: {save_filepath}")
         data = pd.read_csv(save_filepath, index_col="entrez_gene_id")
+        logger.success(f"Successfully loaded RNA-seq data from: {save_filepath}")
         return context_name, data
 
     else:
@@ -138,9 +107,7 @@ def _merge_logical_table(df: pd.DataFrame):
     :return: pandas dataframe of merged table
     """
     # step 1: get all plural ENTREZ_GENE_IDs in the input table, extract unique IDs
-
-    df.reset_index(drop=False, inplace=True)
-    df.dropna(axis=0, subset=["entrez_gene_id"], inplace=True)
+    df.dropna(subset=["entrez_gene_id"], inplace=True)
     df["entrez_gene_id"] = df["entrez_gene_id"].astype(str).str.replace(" /// ", "//").astype(str)
 
     id_list: list[str] = df.loc[~df["entrez_gene_id"].str.contains("//"), "entrez_gene_id"].tolist()  # Collect "single" ids, like "123"
@@ -151,6 +118,7 @@ def _merge_logical_table(df: pd.DataFrame):
     for i in multiple_entrez_ids:
         ids = i.split("//")
         id_list.extend(ids)
+        logger.trace(f"Processing multiple IDs {ids} for {i}")
 
         duplicate_rows = pd.DataFrame([])
         for j in ids:
@@ -160,15 +128,19 @@ def _merge_logical_table(df: pd.DataFrame):
 
         df = pd.concat([df, pd.DataFrame(duplicate_rows)], axis=0, ignore_index=True)
         df.drop(df[df["entrez_gene_id"] == i].index, inplace=True)
+    logger.trace(f"Shape after merging duplciated rows: {df.shape}")
 
     full_entrez_id_sets: set[str] = set()
     entrez_dups_list: list[list[str]] = []
     multi_entrez_index = list(range(len(multiple_entrez_ids)))
 
+    logger.trace("Starting to merge multiple entrez IDs")
     for i in range(len(multiple_entrez_ids)):
         if i not in multi_entrez_index:
             continue
 
+        logger.trace(f"Iterating through multi-entrez ids, index {i}")
+
         set1 = set(multiple_entrez_ids[i].split("//"))
         multi_entrez_index.remove(i)
 
@@ -184,15 +156,17 @@ def _merge_logical_table(df: pd.DataFrame):
         new_entrez_id = " /// ".join(sortlist)
         full_entrez_id_sets.add(new_entrez_id)
 
+    logger.debug(f"Finished merging multiple entrez IDs, found {len(full_entrez_id_sets)} sets")
     entrez_dups_list.extend(i.split(" /// ") for i in full_entrez_id_sets)
     entrez_dups_dict = dict(zip(full_entrez_id_sets, entrez_dups_list, strict=True))
 
+    logger.trace("Replacing IDs in dataframe")
     for merged_entrez_id, entrez_dups_list in entrez_dups_dict.items():
         df["entrez_gene_id"].replace(to_replace=entrez_dups_list, value=merged_entrez_id, inplace=True)
 
-    df.set_index("entrez_gene_id", inplace=True)
     df = df.fillna(-1).groupby(level=0).max()
     df.replace(-1, np.nan, inplace=True)
+    logger.trace(f"Shape after merging: {df.shape}")
 
     # TODO: Test if this is working properly
     """
@@ -208,7 +182,7 @@ def _merge_logical_table(df: pd.DataFrame):
     return df
 
 
-async def _get_transcriptmoic_details(merged_df: pd.DataFrame) -> pd.DataFrame:
+async def _get_transcriptmoic_details(merged_df: pd.DataFrame, taxon_id: int) -> pd.DataFrame:
     """Get details of transcriptomic data.
 
     This function will get the following details of transcriptomic data:
@@ -224,13 +198,16 @@ async def _get_transcriptmoic_details(merged_df: pd.DataFrame) -> pd.DataFrame:
     """
     # If _ExpressedHeaderNames.PROTEOMICS.value is in the dataframe, lower the required expression by 1
     # We are only trying to get details for transcriptomic data
+    logger.debug("Obtaining transcriptomic details")
+    transcriptomic_df: pd.DataFrame = merged_df.copy()
     if _ExpressedHeaderNames.PROTEOMICS in merged_df.columns:
+        logger.trace("Proteomic data found, modifying required and total expression values")
         # Get the number of sources required for a gene to be marked "expressed"
-        required_expression = merged_df["Required"].iloc[0]
+        required_expression = merged_df["required"].iloc[0]
 
         # Subtract 1 from merged_df["TotalExpressed"] if the current value is greater than or equal to 1
         # This is done to take into account the removal of proteomic expression
-        merged_df["TotalExpressed"] = merged_df["TotalExpressed"].apply(lambda x: x - 1 if x >= 1 else x)
+        merged_df["total_expressed"] = merged_df["total_expressed"].apply(lambda x: x - 1 if x >= 1 else x)
 
         # Subtract required_expression by 1 if it is greater than 1
         if required_expression > 1:
@@ -243,590 +220,500 @@ async def _get_transcriptmoic_details(merged_df: pd.DataFrame) -> pd.DataFrame:
             ],
             inplace=False,
         )
+        logger.trace(f"Modified transcriptomic dataframe: {transcriptomic_df.shape}")
 
         # Must recalculate TotalExpressed because proteomic data was removed
         # If the TotalExpressed column is less than the Required column, set active to 1, otherwise set it to 0
         transcriptomic_df.loc[
-            transcriptomic_df["TotalExpressed"] >= transcriptomic_df["Required"],
-            "Active",
+            transcriptomic_df["total_expressed"] >= transcriptomic_df["required"],
+            "active",
         ] = 1
 
-    else:
-        transcriptomic_df: pd.DataFrame = merged_df.copy()
-
-    biodbnet = BioDBNet()
-    gene_details: pd.DataFrame = await biodbnet.async_db2db(
-        values=transcriptomic_df.index.astype(str).values.tolist(),
-        input_db=Input.GENE_ID,
-        output_db=[
-            Output.GENE_SYMBOL,
-            Output.ENSEMBL_GENE_INFO,
-            Output.GENE_INFO,
-        ],
-    )
-    gene_details["entrez_gene_id"] = gene_details.index
-    gene_details.reset_index(drop=True, inplace=True)
-
-    # Apply regex to search for "[Description: XXXXXX]" and retrieve the XXXXXX
-    # It excludes the square brackets and "Description: ", and only returns the description
-    # descriptions: list[str] = [
-    gene_details["description"] = [
-        i.group(1) if isinstance(i, re.Match) else "No Description Available"
-        for i in gene_details["Ensembl Gene Info"].apply(lambda x: re.search(r"\[Description: (.*)\]", x))
-    ]
-
-    gene_details["gene_info_type"] = [
-        i.group(1) if isinstance(i, re.Match) else "None" for i in gene_details["Gene Info"].apply(lambda x: re.search(r"\[Gene Type: (.*)\]", x))
-    ]
-    gene_details["ensembl_info_type"] = [
-        i.group(1) if isinstance(i, re.Match) else "None"
-        for i in gene_details["Ensembl Gene Info"].apply(lambda x: re.search(r"\[Gene Type: (.*)\]", x))
-    ]
-
-    gene_type: list[str] = []
-    row: pd.DataFrame
-    for row in gene_details.itertuples():
-        if row.gene_info_type != "None":
-            gene_type.append(row.gene_info_type)
-        elif row.ensembl_info_type != "None":
-            gene_type.append(row.ensembl_info_type)
-        else:
-            gene_type.append("No Gene Type Available")
-    gene_details["gene_type"] = gene_type
-
-    # Drop gene_info_type and ensembl_info_type columns
-    gene_details.drop(
-        columns=[
-            "Ensembl Gene Info",
-            "Gene Info",
-            "gene_info_type",
-            "ensembl_info_type",
-        ],
-        inplace=True,
+    my_gene = MyGene()
+    gene_details: pd.DataFrame = pd.DataFrame(
+        data=pd.NA,
+        columns=["entrez_gene_id", "gene_symbol", "description", "gene_type"],
+        index=list(range(len(transcriptomic_df))),
     )
-    gene_details.rename(columns={"entrez_gene_id": "Entrez Gene ID"}, inplace=True)
+    logger.trace(f"Querying MyGene for details on {len(transcriptomic_df)} genes")
+    for i, detail in enumerate(
+        await my_gene.query(
+            items=transcriptomic_df["entrez_gene_id"].tolist(),
+            taxon=taxon_id,
+            scopes="entrezgene",
+        )
+    ):
+        gene_details.at[i, "entrez_gene_id"] = detail["entrezgene"]
+        gene_details.at[i, "gene_symbol"] = detail["symbol"]
+        gene_details.at[i, "description"] = detail["name"]
+        gene_details.at[i, "gene_type"] = detail["type_of_gene"]
 
+    logger.debug("Finished obtaining transcriptomic details")
     return gene_details
 
 
 async def _merge_xomics(
     context_name: str,
-    expression_requirement,
-    proteomics_file=None,
-    trnaseq_file=None,
-    mrnaseq_file=None,
-    scrnaseq_file=None,
-    no_hc=False,
-    no_na=False,
+    expression_requirement: int,
+    trna_boolean_matrix: pd.DataFrame | None,
+    mrna_boolean_matrix: pd.DataFrame | None,
+    scrna_boolean_matrix: pd.DataFrame | None,
+    proteomic_boolean_matrix: pd.DataFrame | None,
+    output_merged_filepath: Path,
+    output_gene_activity_filepath: Path,
+    output_transcriptomic_details_filepath: Path,
+    taxon_id: int,
+    force_activate_high_confidence: bool = True,
+    adjust_for_missing_sources: bool = False,
 ):
-    """Merge rnaseq and/or proteomics active genes.
-
-    :param proteomics_file: filename of proteomics config file in /main/data/config_sheets/
-    :param trnaseq_file: filename of Total RNA-seq config file in /main/data/config_sheets/
-    :param mrnaseq_file: filename of mRNA-seq config file in /main/data/config_sheets/
-    :param scrnaseq_file: filename of single-cell RNA-seq config file in /main/data/config_sheets/
-    :param no_hc: True if not adjusting for NA values (happens when gene missing from data source)
-    :param no_na: filename of single-cell RNA-seq config file in /main/data/config_sheets/
-    :param context_name: sheet name to use, should be context, context, cell type, etc
-    :param expression_requirement: integer, minimum number of provided sources with active gene for a it to be in model
-    :return: dictionary where keys are contexts, (tissue name, control type etc) and values are expression tables
-    """
-    config = Config()
-    logger.info(f"Merging data for {context_name}")
-    # load data for each source if it exists. IF not load an empty dummy dataset
-    trnaseq = _load_rnaseq_tests(filename=trnaseq_file, context_name=context_name, prep_method=RNAPrepMethod.TOTAL)
-    mrnaseq = _load_rnaseq_tests(filename=mrnaseq_file, context_name=context_name, prep_method=RNAPrepMethod.MRNA)
-    scrnaseq = _load_rnaseq_tests(filename=scrnaseq_file, context_name=context_name, prep_method=RNAPrepMethod.SCRNA)
-    proteomics = proteomics_gen.load_proteomics_tests(filename=proteomics_file, context_name=context_name)
-
-    expression_list = []
-    high_confidence_list = []
-    merge_data = None
-
-    if trnaseq[0] != "dummy":
-        expression_list.append(_ExpressedHeaderNames.TRNASEQ)
-        high_confidence_list.append(_HighExpressionHeaderNames.TRNASEQ)
-        trnaseq_data = trnaseq[1].loc[:, ["expressed", "high"]]
-        trnaseq_data.rename(
-            columns={
-                "expressed": _ExpressedHeaderNames.TRNASEQ,
-                "high": _HighExpressionHeaderNames.TRNASEQ,
-            },
-            inplace=True,
-        )
-        merge_data = trnaseq_data
-
-    if mrnaseq[0] != "dummy":
-        expression_list.append(_ExpressedHeaderNames.MRNASEQ)
-        high_confidence_list.append(_HighExpressionHeaderNames.MRNASEQ)
-        mrnaseq_data = mrnaseq[1].loc[:, ["expressed", "high"]]
-        mrnaseq_data.rename(
-            columns={
-                "expressed": _ExpressedHeaderNames.MRNASEQ,
-                "high": _HighExpressionHeaderNames.MRNASEQ,
-            },
-            inplace=True,
-        )
-        merge_data = mrnaseq_data if merge_data is None else merge_data.join(mrnaseq_data, how="outer")
-
-    if scrnaseq[0] != "dummy":
-        expression_list.append(_ExpressedHeaderNames.SCRNASEQ)
-        high_confidence_list.append(_HighExpressionHeaderNames.SCRNASEQ)
-        scrnaseq_data = scrnaseq[1].loc[:, ["expressed", "high"]]
-        scrnaseq_data.rename(
-            columns={
-                "expressed": _ExpressedHeaderNames.SCRNASEQ,
-                "high": _HighExpressionHeaderNames.SCRNASEQ,
-            },
-            inplace=True,
-        )
-        merge_data = scrnaseq_data if merge_data is None else merge_data.join(scrnaseq_data, how="outer")
-
-    if proteomics[0] != "dummy":
-        expression_list.append(_ExpressedHeaderNames.PROTEOMICS)
-        high_confidence_list.append(_HighExpressionHeaderNames.PROTEOMICS)
-        prote_data = proteomics[1].loc[:, ["expressed", "high"]]
-        prote_data.rename(
-            columns={
-                "expressed": _ExpressedHeaderNames.PROTEOMICS,
-                "high": _HighExpressionHeaderNames.PROTEOMICS,
-            },
-            inplace=True,
-        )
-        merge_data = prote_data if merge_data is None else merge_data.join(prote_data, how="outer")
+    logger.debug(f"Starting to merge data sources for context '{context_name}'")
+    expression_list: list[str] = []
+    high_confidence_list: list[str] = []
+    merge_data: pd.DataFrame = pd.DataFrame()
+
+    for matrix, expressed_sourcetype, high_expressed_sourcetype in (
+        (trna_boolean_matrix, _ExpressedHeaderNames.TRNASEQ, _HighExpressionHeaderNames.TRNASEQ),
+        (mrna_boolean_matrix, _ExpressedHeaderNames.MRNASEQ, _HighExpressionHeaderNames.MRNASEQ),
+        (scrna_boolean_matrix, _ExpressedHeaderNames.SCRNASEQ, _HighExpressionHeaderNames.SCRNASEQ),
+        (proteomic_boolean_matrix, _ExpressedHeaderNames.PROTEOMICS, _HighExpressionHeaderNames.PROTEOMICS),
+    ):
+        if matrix is None:
+            logger.trace(f"Skipping {expressed_sourcetype} because it's matrix does not exist")
+            continue
+
+        matrix: pd.DataFrame  # re-define type to assist in type hinting for IDEs
+        expression_list.append(expressed_sourcetype)
+        high_confidence_list.append(high_expressed_sourcetype)
+        matrix.rename(columns={"expressed": expressed_sourcetype, "high": high_expressed_sourcetype}, inplace=True)
+        merge_data = matrix if merge_data.empty else merge_data.merge(matrix, on="entrez_gene_id", how="outer")
 
-    if merge_data is None:
-        logger.critical(f"No data is available for the '{context_name}' context. If this is intentional, ignore this error.")
+    logger.trace(f"Shape of merged data before merging logical tables: {merge_data.shape}")
+    if merge_data.empty:
+        logger.warning(f"No data is available for the '{context_name}' context. If this is intentional, ignore this error.")
         return {}
-    merge_data = _merge_logical_table(merge_data)
 
+    merge_data = _merge_logical_table(merge_data)
+    logger.debug(f"Shape of merged data after merging logical table: {merge_data.shape}")
     num_sources = len(expression_list)
-    merge_data["Active"] = 0
-    merge_data["Required"] = 0
+    merge_data["active"] = 0
+    merge_data["required"] = 0
 
-    if no_na:  # dont adjust for na values
-        merge_data.loc[:, "Required"] = merge_data[expression_list].apply(
-            lambda x: expression_requirement if (expression_requirement - (num_sources - x.count()) > 0) else 1,
-            axis=1,
-        )
-    else:  # subtract one from requirement per NA
-        merge_data.loc[:, "Required"] = merge_data[expression_list].apply(
+    logger.trace(f"Number of data sources: {num_sources}")
+    if adjust_for_missing_sources:  # Subtract 1 from requirement per missing source
+        logger.trace("Adjusting for missing data sources")
+        merge_data.loc[:, "required"] = merge_data[expression_list].apply(
             lambda x: expression_requirement - (num_sources - x.count()) if (expression_requirement - (num_sources - x.count()) > 0) else 1,
             axis=1,
         )
+    else:  # Do not adjust for missing sources
+        logger.trace("Not adjusting for missing data sources")
+        merge_data.loc[:, "required"] = merge_data[expression_list].apply(
+            lambda x: expression_requirement if (expression_requirement - (num_sources - x.count()) > 0) else 1, axis=1
+        )
+    logger.trace("Created expression requirement column")
+
+    # Count the number of sources each gene is active in
+    # set to active in final output if we meet the adjusted expression requirement
+    merge_data["total_expressed"] = merge_data[expression_list].sum(axis=1)
+    merge_data.loc[merge_data["total_expressed"] >= merge_data["required"], "active"] = 1
+    logger.trace("Created total expression requirement column")
+
+    if force_activate_high_confidence:  # If a gene is high-confidence in at least 1 data source, set it to active
+        logger.trace("Forcing high confidence genes")
+        merge_data.loc[merge_data[high_confidence_list].sum(axis=1) > 0, "active"] = 1
+
+    merge_data.dropna(inplace=True)
+    merge_data.to_csv(output_merged_filepath, index=False)
+    logger.success(f"Saved merged data to {output_merged_filepath}")
+
+    logger.debug(f"Generating transcriptomic details using {output_merged_filepath}")
+    transcriptomic_details = await _get_transcriptmoic_details(merge_data, taxon_id=taxon_id)
+    logger.debug(f"Saving transcriptomic details to {output_transcriptomic_details_filepath}")
+    transcriptomic_details.dropna(inplace=True)
+    transcriptomic_details.to_csv(output_transcriptomic_details_filepath, index=False)
+    logger.success(f"Saved transcriptomic details to {output_transcriptomic_details_filepath}")
+    return {context_name: output_gene_activity_filepath.as_posix()}
+
+
+async def _update_missing_data(input_matrices: _InputMatrices, taxon_id: int) -> _InputMatrices:
+    logger.trace("Updating missing genomic data")
+    matrix_keys: dict[str, list[pd.DataFrame]] = {
+        "trna": [input_matrices.trna],
+        "mrna": [input_matrices.mrna],
+        "scrna": [input_matrices.scrna],
+        "proteomics": [input_matrices.proteomics],
+    }
+    logger.trace(f"Gathering missing data for data sources: {','.join(key for key in matrix_keys if key is not None)}")
+    # fmt: off
+    results = await asyncio.gather(
+        *[
+            # Using 'is not None' is required because the truth value of a Dataframe is ambiguous
+            get_missing_gene_data(values=input_matrices.trna, taxon_id=taxon_id) if input_matrices.trna is not None else asyncio.sleep(0),
+            get_missing_gene_data(values=input_matrices.mrna, taxon_id=taxon_id) if input_matrices.mrna is not None else asyncio.sleep(0),
+            get_missing_gene_data(values=input_matrices.scrna, taxon_id=taxon_id) if input_matrices.scrna is not None else asyncio.sleep(0),
+            get_missing_gene_data(values=input_matrices.proteomics, taxon_id=taxon_id) if input_matrices.proteomics is not None else asyncio.sleep(0),
+        ]
+    )
+    # fmt: on
+    for i, key in enumerate(matrix_keys):
+        matrix_keys[key].append(results[i])
+
+    for matrix_name, (matrix, conversion) in matrix_keys.items():
+        matrix: pd.DataFrame
+        if matrix is not None:
+            # fmt: off
+            existing_data = (
+                "gene_symbol" if "gene_symbol" in matrix
+                else "entrez_gene_id" if "entrez_gene_id" in matrix
+                else "ensembl_gene_id"
+            )
+            # fmt: on
+            logger.trace(f"Merging conversion data for {matrix_name}, existing id column is: {existing_data}")
+            input_matrices[matrix_name] = (
+                input_matrices[matrix_name].merge(conversion, how="left", on=[existing_data]).dropna().reset_index(drop=True)
+            )
 
-    # count number of sources gene is active in. Set to active in final output if at least adjusted expression reqirmnt
-    merge_data["TotalExpressed"] = merge_data[expression_list].sum(axis=1)
-    merge_data.loc[merge_data["TotalExpressed"] >= merge_data["Required"], "Active"] = 1
-
-    if not no_hc:  # set genes that are high-confidence in at least one data source to active
-        merge_data.loc[merge_data[high_confidence_list].sum(axis=1) > 0, "Active"] = 1
-
-    # merge_data = merge_data.astype(int)
-    merge_data = merge_data
-
-    filepath = config.result_dir / context_name / f"merged_{context_name}.csv"
-    merge_data.to_csv(filepath, index_label="entrez_gene_id")
-
-    filepath = config.result_dir / context_name / f"ActiveGenes_{context_name}_Merged.csv"
-    merge_data.reset_index(drop=False, inplace=True)
-
-    split_entrez = split_gene_expression_data(merge_data)
-    split_entrez.to_csv(filepath, index_label="entrez_gene_id")
-    files_dict = {context_name: filepath.as_posix()}
-
-    transcriptomic_details = await _get_transcriptmoic_details(merge_data)
-    transcriptomic_details_filepath = filepath.parent / f"TranscriptomicDetails_{context_name}.csv"
-    transcriptomic_details.to_csv(transcriptomic_details_filepath, index=False)
-
-    logger.success(f"{context_name}: Save to {filepath}\n")
-
-    return files_dict
+    logger.debug("Updated missing genomic data")
+    return input_matrices
 
 
-async def _handle_context_batch(  # noqa: C901
-    trnaseq_file: Path | None,
-    mrnaseq_file: Path | None,
-    scrnaseq_file: Path | None,
-    proteomics_file: Path | None,
-    tweight: float,
-    mweight: float,
-    sweight: float,
-    pweight: float,
+async def _process(
+    *,
+    context_name: str,
+    input_matrices: _InputMatrices,
+    boolean_matrices: _InputMatrices,
+    batch_names: _BatchNames,
+    source_weights: _SourceWeights,
+    taxon_id: int,
+    minimum_source_expression: int,
     expression_requirement: int,
+    weighted_z_floor: int,
+    weighted_z_ceiling: int,
     adjust_method: AdjustmentMethod,
-    no_hc: bool,
-    no_na: bool,
     merge_zfpkm_distribution: bool,
-    keep_gene_score: bool,
+    force_activate_high_confidence: bool,
+    adjust_for_missing_sources: bool,
+    output_merge_activity_filepath: Path,
+    output_transcriptomic_details_filepath: Path,
+    output_activity_filepaths: _OutputCombinedSourceFilepath,
+    output_final_model_scores_filepath: Path,
+    output_figure_dirpath: Path | None,
 ):
     """Merge different data sources for each context type."""
-    if all(file is None for file in [trnaseq_file, mrnaseq_file, scrnaseq_file, proteomics_file]):
-        raise ValueError("No configuration file was passed!")
+    logger.trace(
+        f"Settings: Min Expression: {minimum_source_expression}, Expression Requirement: {expression_requirement}, "
+        f"Weighted Z-Score Floor: {weighted_z_floor}, Weighted Z-Score Ceiling: {weighted_z_ceiling}, "
+        f"Adjust Method: {adjust_method.value}, Merge Z-Scores: {merge_zfpkm_distribution}, "
+        f"Force High Confidence: {force_activate_high_confidence}, Adjust for Missing: {adjust_for_missing_sources}"
+    )
 
-    config = Config()
-    sheet_names = []
-    for file in [trnaseq_file, mrnaseq_file, scrnaseq_file, proteomics_file]:
-        if file is not None:
-            config_filepath = config.config_dir / file
-            try:
-                xl = pd.ExcelFile(config_filepath, engine="openpyxl")
-            except Exception as e:
-                raise ValueError(f"Unable to read file '{config_filepath}'") from e
-            sheet_names += xl.sheet_names
-
-    use_trna = trnaseq_file is not None
-    use_mrna = mrnaseq_file is not None
-    use_scrna = scrnaseq_file is not None
-    use_proteins = proteomics_file is not None
-
-    counts = Counter(sheet_names)
-    sheet_names = sorted(set(sheet_names))
-    logger.info("Beginning to merge data within contexts")
-    dict_list = {}
-
-    max_inputs = max(counts.values())
-    min_inputs = min(counts.values())
+    # Collect missing genomic data for each of the input items in asynchronous parallel
+    input_matrices = await _update_missing_data(input_matrices, taxon_id)
+    logger.trace("Missing data updated")
 
     if merge_zfpkm_distribution:
-        logger.debug("Using zFPKM distribution for merging")
-        _combine_zscores(
-            working_dir=config.result_dir.as_posix(),
-            context_names=sheet_names,
-            global_use_mrna=use_mrna,
-            global_use_trna=use_trna,
-            global_use_scrna=use_scrna,
-            global_use_proteins=use_proteins,
-            keep_gene_scores=keep_gene_score,
-            global_trna_weight=tweight,
-            global_mrna_weight=mweight,
-            global_scrna_weight=sweight,
-            global_protein_weight=pweight,
+        logger.trace("Merging Z-Scores")
+        await _begin_combining_distributions(
+            context_name=context_name,
+            input_matrices=input_matrices,
+            batch_names=batch_names,
+            source_weights=source_weights,
+            output_filepaths=output_activity_filepaths,
+            output_figure_dirpath=output_figure_dirpath,
+            output_final_model_scores=output_final_model_scores_filepath,
+            weighted_z_floor=weighted_z_floor,
+            weighted_z_ceiling=weighted_z_ceiling,
         )
-
-    for context_name in sheet_names:
-        num_sources = counts[context_name]
-        match adjust_method:
-            case AdjustmentMethod.PROGRESSIVE:
-                adjusted_expression_requirement = (num_sources - min_inputs) + expression_requirement
-            case AdjustmentMethod.REGRESSIVE:
-                adjusted_expression_requirement = expression_requirement - (max_inputs - num_sources)
-            case AdjustmentMethod.FLAT:
-                adjusted_expression_requirement = expression_requirement
-            case _:
-                adjusted_expression_requirement = int(custom_df.iloc[custom_df["context"] == context_name, "req"].iloc[0])
-
-        if adjusted_expression_requirement != expression_requirement:
-            logger.debug(
-                f"Expression requirement of '{expression_requirement}' adjusted to "
-                f"'{adjusted_expression_requirement}' using '{adjust_method.value}' adjustment method "
-                f"for '{context_name}'."
-            )
-
-        if adjusted_expression_requirement > num_sources:
-            logger.warning(
-                f"Expression requirement for {context_name} was calculated to be greater "
-                f"than max number of input data sources. "
-                f"Will be force changed to {num_sources} to prevent output from having 0 active genes. "
-                f"Consider lowering the expression requirement or changing the adjustment method."
-            )
-            adjusted_expression_requirement = num_sources
-
-        if adjusted_expression_requirement < 1:  # never allow expression requirement to be less than one
-            logger.warning(
-                f"Expression requirement for {context_name} was calculated to be less than 1. "
-                "Will be changed to 1 to prevent output from having 0 active genes. "
-            )
-            adjusted_expression_requirement = 1
-
-        files_dict = await _merge_xomics(
-            context_name,
-            expression_requirement=adjusted_expression_requirement,
-            proteomics_file=proteomics_file,
-            trnaseq_file=trnaseq_file,
-            mrnaseq_file=mrnaseq_file,
-            scrnaseq_file=scrnaseq_file,
-            no_hc=no_hc,
-            no_na=no_na,
+        logger.trace("Finished merging Z-Scores")
+
+    # the more data sources available, the higher the expression requirement for the gene
+    num_sources = sum(1 for source in input_matrices if source is not None)
+    if adjust_method == AdjustmentMethod.PROGRESSIVE:
+        adjusted_expression_requirement = (num_sources - minimum_source_expression) + expression_requirement
+    # the more data sources available, the lower the expression requirement for the gene
+    elif adjust_method == AdjustmentMethod.REGRESSIVE:
+        # we use a hardcoded 4 here because that is the maximum number of contexts available
+        # (trna, mrna, scrna, and proteomics is 4 sources)
+        adjusted_expression_requirement = expression_requirement - (4 - num_sources)
+    elif adjust_method == AdjustmentMethod.FLAT:
+        adjusted_expression_requirement = expression_requirement
+    logger.debug(f"Adjusted expression requirement: {adjusted_expression_requirement}")
+
+    if adjusted_expression_requirement != expression_requirement:
+        logger.debug(
+            f"Expression requirement of '{expression_requirement}' adjusted to "
+            f"'{adjusted_expression_requirement}' using '{adjust_method.value}' adjustment method "
+            f"for '{context_name}'."
         )
 
-        dict_list |= files_dict
-
-    files_json = config.result_dir / "step1_results_files.json"
-    files_json.parent.mkdir(parents=True, exist_ok=True)
-    with files_json.open("w") as f:
-        json.dump(dict_list, f)  # type: ignore
-
-    return
-
+    if adjusted_expression_requirement > num_sources:
+        logger.warning(
+            f"Expression requirement for {context_name} was calculated to be greater "
+            f"than max number of input data sources. "
+            f"Will be force changed to {num_sources} to prevent output from having 0 active genes. "
+            f"Consider lowering the expression requirement or changing the adjustment method."
+        )
+        adjusted_expression_requirement = num_sources
 
-async def merge_xomics(
-    trnaseq_filepath: str | Path | None = None,
-    mrnaseq_filepath: str | Path | None = None,
-    scrnaseq_filepath: str | Path | None = None,
-    proteomics_filepath: str | Path | None = None,
-    trna_weight: float = 1,
-    mrna_weight: float = 1,
-    scrna_weight: float = 1,
-    proteomics_weight: float = 2,
-    expression_requirement: int | None = None,
-    adjust_method: AdjustmentMethod = AdjustmentMethod.FLAT,
-    no_high_confidence: bool = False,
-    no_na: bool = False,
-    merge_zfpkm_distribution: bool = False,
-    keep_transcriptomics_score: bool = True,
-):
-    """Merge expression tables of multiple sources (RNA-seq, proteomics) into one."""
-    if expression_requirement is None:
-        expression_requirement = sum(
-            test is not None
-            for test in [
-                trnaseq_filepath,
-                mrnaseq_filepath,
-                scrnaseq_filepath,
-                proteomics_filepath,
-            ]
+    if adjusted_expression_requirement < 1:  # never allow expression requirement to be less than one
+        logger.warning(
+            f"Expression requirement for {context_name} was calculated to be less than 1. "
+            "Will be changed to 1 to prevent output from having 0 active genes. "
         )
-    elif expression_requirement < 1:
-        raise ValueError("Expression requirement must be at least 1!")
-
-    trnaseq_filepath = Path(trnaseq_filepath) if trnaseq_filepath else None
-    mrnaseq_filepath = Path(mrnaseq_filepath) if mrnaseq_filepath else None
-    scrnaseq_filepath = Path(scrnaseq_filepath) if scrnaseq_filepath else None
-    proteomics_filepath = Path(proteomics_filepath) if proteomics_filepath else None
-
-    await _handle_context_batch(
-        trnaseq_filepath,
-        mrnaseq_filepath,
-        scrnaseq_filepath,
-        proteomics_filepath,
-        trna_weight,
-        mrna_weight,
-        scrna_weight,
-        proteomics_weight,
-        expression_requirement,
-        adjust_method,
-        no_high_confidence,
-        no_na,
-        merge_zfpkm_distribution,
-        keep_transcriptomics_score,
+        adjusted_expression_requirement = 1
+
+    logger.debug(f"Final Expression Requirement: {adjusted_expression_requirement}")
+    await _merge_xomics(
+        context_name=context_name,
+        expression_requirement=adjusted_expression_requirement,
+        trna_boolean_matrix=boolean_matrices.trna,
+        mrna_boolean_matrix=boolean_matrices.mrna,
+        scrna_boolean_matrix=boolean_matrices.scrna,
+        proteomic_boolean_matrix=boolean_matrices.proteomics,
+        output_merged_filepath=output_merge_activity_filepath,
+        output_gene_activity_filepath=output_final_model_scores_filepath,
+        output_transcriptomic_details_filepath=output_transcriptomic_details_filepath,
+        taxon_id=taxon_id,
+        force_activate_high_confidence=force_activate_high_confidence,
+        adjust_for_missing_sources=adjust_for_missing_sources,
     )
 
 
-def _parse_args() -> _Arguments:
-    """Merge expression tables of multiple sources, (RNA-seq, proteomics) into one list.
-
-    User can specify the number of sources with an active gene in order for it to be considered active in the model.
-    Otherwise, it defaults to the number of sources provided. High-confidence genes from any source will be considered
-    active in the model, regardless of agreement with other sources.
-    """
-    parser = argparse.ArgumentParser(
-        prog="merge_xomics.py",
-        description="Merge expression tables of multiple sources (RNA-seq, proteomics) into one",
-        epilog="For additional help, please post questions/issues in the MADRID GitHub repo at "
-        "https://github.com/HelikarLab/MADRID or email babessell@gmail.com",
-    )
+def _build_batches(
+    trna_metadata: pd.DataFrame | None,
+    mrna_metadata: pd.DataFrame | None,
+    scrna_metadata: pd.DataFrame | None,
+    proteomic_metadata: pd.DataFrame | None,
+) -> _BatchNames:
+    batch_names = _BatchNames(**{source.name.lower(): [] for source in SourceTypes})
+    for source, metadata in zip(SourceTypes, [trna_metadata, mrna_metadata, scrna_metadata, proteomic_metadata], strict=True):
+        source: SourceTypes
+        metadata: pd.DataFrame
+        if metadata is None:
+            logger.trace(f"Metadata for source '{source.value}' is None, skipping")
+            continue
 
-    parser.add_argument(
-        "-d",
-        "--merge-zfpkm-distribution",
-        action="store_true",
-        required=False,
-        default=False,
-        dest="merge_zfpkm_distribution",
-        help="Flag to merge zFPKM distributions. Required if using iMAT reconstruction algorithm in "
-        "create_context_specific_model.py. Must have run rnaseq_gen.py with 'zFPKM' as "
-        "'--technique'. If --proteomics-config-file is given will merge proteomics distributions "
-        "with zFPKM distributions using a weighted scheme.",
-    )
+        metadata: pd.DataFrame  # Re-assign type to assist in type hinting
+        for batch_num, study in enumerate(sorted(metadata["study"].unique()), start=1):
+            study_sample_names = metadata[metadata["study"] == study]["sample_name"].tolist()
+            batch_names[source.value].append(_BatchEntry(batch_num=batch_num, sample_names=study_sample_names))
+            logger.debug(f"Found {len(study_sample_names)} sample names for study '{study}', batch number {batch_num}")
+    return batch_names
 
-    parser.add_argument(
-        "-k",
-        "--keep-transcriptomics-scores",
-        action="store_true",
-        required=False,
-        default=True,
-        dest="keep_transcriptomics_scores",
-        help="When merging z-score distributions of expression, if using both protein abundance and transcipt zFPKM "
-        "flag true if you wish to keep z-score of genes with no protein data, flag false if you wish to discard "
-        "and treat as no expression",
-    )
 
-    parser.add_argument(
-        "-t",
-        "--total-rnaseq-config-file",
-        type=str,
-        required=False,
-        default=None,
-        dest="trnaseq_filename",
-        help="Name of total RNA-seq config .xlsx file in the /main/data/config_files/.",
-    )
+def _validate_source_arguments(
+    source: SourceTypes,
+    *args,
+) -> None:
+    """Validate arguments for each source are valid.
 
-    parser.add_argument(
-        "-m",
-        "--mrnaseq-config-file",
-        type=str,
-        required=False,
-        default=None,
-        dest="mrnaseq_filename",
-        help="Name of mRNA-seq config .xlsx file in the /main/data/config_files/.",
-    )
+    If at least one input item is provided, validate that all required items are also present.
 
-    parser.add_argument(
-        "-s",
-        "--scrnaseq-config-file",
-        type=str,
-        required=False,
-        default=None,
-        dest="scrnaseq_filename",
-        help="Name of RNA-seq config .xlsx file in the /main/data/config_files/.",
-    )
+    :param matrix_or_filepath: The gene count matrix or filepath
+    :param boolean_matrix_or_filepath: The boolean matrix of gene activities
+    :param metadata_filepath_or_df: Dataframe or filepath to sample metadata
+    :param output_activity_filepath: Output filepath location
+    :param source: Source type
 
-    parser.add_argument(
-        "-p",
-        "--proteomics-config-file",
-        type=str,
-        required=False,
-        default=None,
-        dest="proteomics_filename",
-        help="Name of proteomics config .xlsx file in the /main/data/config_files/.",
-    )
+    """
+    if any(i for i in args) and not all(i for i in args):
+        _log_and_raise_error(
+            f"Must specify all or none of '{source.value}' arguments",
+            error=ValueError,
+            level=LogLevel.ERROR,
+        )
 
-    parser.add_argument(
-        "-e",
-        "--expression-requirement",
-        type=str,
-        required=False,
-        default=None,
-        dest="expression_requirement",
-        help="Number of sources with active gene for it to be considered active even if it is not a high confidence-gene",
-    )
 
-    parser.add_argument(
-        "-r",
-        "--requirement-adjust",
-        type=str,
-        required=False,
-        default="flat",
-        dest="adjustment_method",
-        help="Technique to adjust expression requirement based on differences in number of provided data source types.",
-    )
+async def merge_xomics(  # noqa: C901
+    context_name: str,
+    output_merge_activity_filepath: Path,
+    output_transcriptomic_details_filepath: Path,
+    output_final_model_scores_filepath: Path,
+    output_figure_dirpath: Path | None,
+    taxon_id: int,
+    trna_matrix_or_filepath: Path | pd.DataFrame | None = None,
+    mrna_matrix_or_filepath: Path | pd.DataFrame | None = None,
+    scrna_matrix_or_filepath: Path | pd.DataFrame | None = None,
+    proteomic_matrix_or_filepath: Path | pd.DataFrame | None = None,
+    trna_boolean_matrix_or_filepath: Path | pd.DataFrame | None = None,
+    mrna_boolean_matrix_or_filepath: Path | pd.DataFrame | None = None,
+    scrna_boolean_matrix_or_filepath: Path | pd.DataFrame | None = None,
+    proteomic_boolean_matrix_or_filepath: Path | pd.DataFrame | None = None,
+    trna_metadata_filepath_or_df: Path | pd.DataFrame | None = None,
+    mrna_metadata_filepath_or_df: Path | pd.DataFrame | None = None,
+    scrna_metadata_filepath_or_df: Path | pd.DataFrame | None = None,
+    proteomic_metadata_filepath_or_df: Path | pd.DataFrame | None = None,
+    output_trna_activity_filepath: Path | None = None,
+    output_mrna_activity_filepath: Path | None = None,
+    output_scrna_activity_filepath: Path | None = None,
+    output_proteomic_activity_filepath: Path | None = None,
+    trna_weight: int = 1,
+    mrna_weight: int = 1,
+    scrna_weight: int = 1,
+    proteomic_weight: int = 2,
+    minimum_source_expression: int = 1,
+    expression_requirement: int | None = None,
+    adjust_method: AdjustmentMethod = AdjustmentMethod.FLAT,
+    force_activate_high_confidence: bool = False,
+    adjust_for_na: bool = False,
+    merge_zfpkm_distribution: bool = False,
+    weighted_z_floor: int = -6,
+    weighted_z_ceiling: int = 6,
+    log_level: LogLevel = LogLevel.INFO,
+    log_location: str | TextIOWrapper = sys.stderr,
+):
+    """Merge expression tables of multiple sources (RNA-seq, proteomics) into one."""
+    _set_up_logging(level=log_level, location=log_location)
+    logger.info(f"Starting to merge all omics data for context: '{context_name}'")
+
+    # fmt: off
+    source_data = {
+        SourceTypes.TRNA: (trna_matrix_or_filepath, trna_boolean_matrix_or_filepath, trna_metadata_filepath_or_df, output_trna_activity_filepath),
+        SourceTypes.MRNA: (mrna_matrix_or_filepath, mrna_boolean_matrix_or_filepath, mrna_metadata_filepath_or_df, output_mrna_activity_filepath),
+        SourceTypes.SCRNA: (scrna_matrix_or_filepath, scrna_boolean_matrix_or_filepath, scrna_metadata_filepath_or_df, output_scrna_activity_filepath),  # noqa: E501
+        SourceTypes.PROTEOMICS: (proteomic_matrix_or_filepath, proteomic_boolean_matrix_or_filepath, proteomic_metadata_filepath_or_df, output_proteomic_activity_filepath),  # noqa: E501
+    }
+    # fmt: on
+    for source in source_data:
+        _validate_source_arguments(source, source_data[source])
+
+    if all(
+        file is None
+        for file in (
+            trna_matrix_or_filepath,
+            mrna_matrix_or_filepath,
+            scrna_matrix_or_filepath,
+            proteomic_matrix_or_filepath,
+        )
+    ):
+        _log_and_raise_error("No data was passed!", error=ValueError, level=LogLevel.ERROR)
+
+    if adjust_method not in AdjustmentMethod:
+        _log_and_raise_error(
+            f"Adjustment method must be one of {AdjustmentMethod}; got: {adjust_method}",
+            error=ValueError,
+            level=LogLevel.ERROR,
+        )
 
-    parser.add_argument(
-        "-c",
-        "--custom-requirement-file",
-        required="custom" in sys.argv,  # required if --requriement-adjust is "custom",
-        dest="custom_expression_filename",
-        default=None,
-        help="Name of .xlsx file where first column is context names and second column is expression requirement for that context",
-    )
+    if expression_requirement < 1:
+        logger.warning(f"Expression requirement must be at least 1! Setting to the minimum of 1 now. Got: {expression_requirement}")
+        expression_requirement = 1
 
-    parser.add_argument(
-        "--no-hc",
-        "--no-high-confidence",
-        action="store_true",
-        required=False,
-        default=False,
-        dest="no_high_confidence",
-        help="Prevent high-confidence genes forcing a gene to be used in final model, irrespective of other other data sources",
+    if expression_requirement is None:
+        expression_requirement = sum(
+            test is not None
+            for test in (
+                trna_matrix_or_filepath,
+                mrna_matrix_or_filepath,
+                scrna_matrix_or_filepath,
+                proteomic_matrix_or_filepath,
+            )
+        )
+        logger.debug(f"Expression requirement not specified; setting to {expression_requirement}")
+
+    output_final_model_scores_filepath.parent.mkdir(parents=True, exist_ok=True)
+    if output_merge_activity_filepath:
+        output_merge_activity_filepath.parent.mkdir(parents=True, exist_ok=True)
+    if output_transcriptomic_details_filepath:
+        output_transcriptomic_details_filepath.parent.mkdir(parents=True, exist_ok=True)
+    if output_trna_activity_filepath:
+        output_trna_activity_filepath.parent.mkdir(parents=True, exist_ok=True)
+    if output_mrna_activity_filepath:
+        output_mrna_activity_filepath.parent.mkdir(parents=True, exist_ok=True)
+    if output_scrna_activity_filepath:
+        output_scrna_activity_filepath.parent.mkdir(parents=True, exist_ok=True)
+    if output_proteomic_activity_filepath:
+        output_proteomic_activity_filepath.parent.mkdir(parents=True, exist_ok=True)
+    if output_figure_dirpath:
+        output_figure_dirpath.mkdir(parents=True, exist_ok=True)
+
+    # Build trna items
+    trna_matrix: pd.DataFrame | None
+    trna_boolean_matrix: pd.DataFrame | None
+    trna_metadata: pd.DataFrame | None
+    trna_matrix, trna_boolean_matrix, trna_metadata = await asyncio.gather(
+        *[
+            _read_file(trna_matrix_or_filepath),
+            _read_file(trna_boolean_matrix_or_filepath),
+            _read_file(trna_metadata_filepath_or_df),
+        ]
     )
 
-    parser.add_argument(
-        "-na",
-        "--no-na-adjustment",
-        action="store_true",
-        required=False,
-        default=False,
-        dest="no_na",
-        help="Prevent genes missing in a data source library, but present in others from "
-        "subtracting 1 from the expression requirement per data source that gene is missing in",
+    # Build mrna items
+    mrna_matrix: pd.DataFrame | None
+    mrna_boolean_matrix: pd.DataFrame | None
+    mrna_metadata: pd.DataFrame | None
+    mrna_matrix, mrna_boolean_matrix, mrna_metadata = await asyncio.gather(
+        *[
+            _read_file(mrna_matrix_or_filepath),
+            _read_file(mrna_boolean_matrix_or_filepath),
+            _read_file(mrna_metadata_filepath_or_df),
+        ]
     )
 
-    parser.add_argument(
-        "-tw",
-        "--total-rnaseq-weight",
-        required=False,
-        default=1,
-        type=float,
-        dest="trna_weight",
-        help="Total RNA-seq weight for merging zFPKM distribution",
+    # build scrna items
+    scrna_matrix: pd.DataFrame | None
+    scrna_boolean_matrix: pd.DataFrame | None
+    scrna_metadata: pd.DataFrame | None
+    scrna_matrix, scrna_boolean_matrix, scrna_metadata = await asyncio.gather(
+        *[
+            _read_file(scrna_matrix_or_filepath),
+            _read_file(scrna_boolean_matrix_or_filepath),
+            _read_file(scrna_metadata_filepath_or_df),
+        ]
     )
 
-    parser.add_argument(
-        "-mw",
-        "--mrnaseq-weight",
-        required=False,
-        default=1,
-        type=float,
-        dest="mrna_weight",
-        help="PolyA enriched (messenger) RNA-seq weight for merging zFPKM distribution",
+    # build proteomic items
+    proteomic_matrix: pd.DataFrame | None
+    proteomic_boolean_matrix: pd.DataFrame | None
+    proteomic_metadata: pd.DataFrame | None
+    proteomic_matrix, proteomic_boolean_matrix, proteomic_metadata = await asyncio.gather(
+        *[
+            _read_file(proteomic_matrix_or_filepath),
+            _read_file(proteomic_boolean_matrix_or_filepath),
+            _read_file(proteomic_metadata_filepath_or_df),
+        ]
     )
 
-    parser.add_argument(
-        "-sw",
-        "--single-cell-rnaseq-weight",
-        required=False,
-        default=1,
-        type=float,
-        dest="scrna_weight",
-        help="Single-cell RNA-seq weight for merging zFPKM distribution",
+    source_weights = _SourceWeights(trna=trna_weight, mrna=mrna_weight, scrna=scrna_weight, proteomics=proteomic_weight)
+    input_matrices = _InputMatrices(trna=trna_matrix, mrna=mrna_matrix, scrna=scrna_matrix, proteomics=proteomic_matrix)
+    boolean_matrices = _InputMatrices(
+        trna=trna_boolean_matrix,
+        mrna=mrna_boolean_matrix,
+        scrna=scrna_boolean_matrix,
+        proteomics=proteomic_boolean_matrix,
     )
-
-    parser.add_argument(
-        "-pw",
-        "--protein-weight",
-        required=False,
-        default=2,
-        type=float,
-        dest="proteomics_weight",
-        help="Proteomics weight for merging z-score distribution",
+    output_activity_filepaths = _OutputCombinedSourceFilepath(
+        trna=output_trna_activity_filepath,
+        mrna=output_mrna_activity_filepath,
+        scrna=output_scrna_activity_filepath,
+        proteomics=output_proteomic_activity_filepath,
     )
-    args = parser.parse_args()
-    args.adjustment_method = AdjustmentMethod.from_string(str(args.adjustment_method))
-    return _Arguments(**vars(args))
-
-
-if __name__ == "__main__":
-    import asyncio
-
-    args = _parse_args()
-    config = Config()
-
-    # read custom expression requirment file if used
-    if args.custom_expression_filename:
-        custom_filepath = config.data_dir / args.custom_expression_filename
-        custom_df = pd.read_excel(custom_filepath, sheet_name=0)
-        custom_df.columns = ["context", "req"]
-    else:
-        custom_df = pd.DataFrame([])
-
-    def_exp_req = sum(
-        test is None
-        for test in [
-            args.trnaseq_filename,
-            args.mrnaseq_filename,
-            args.scrnaseq_filename,
-            args.proteomics_filename,
-        ]
+    batch_names = _build_batches(
+        trna_metadata=trna_metadata,
+        mrna_metadata=mrna_metadata,
+        scrna_metadata=scrna_metadata,
+        proteomic_metadata=proteomic_metadata,
     )
 
-    asyncio.run(
-        merge_xomics(
-            trnaseq_filepath=args.trnaseq_filename,
-            mrnaseq_filepath=args.mrnaseq_filename,
-            scrnaseq_filepath=args.scrnaseq_filename,
-            proteomics_filepath=args.proteomics_filename,
-            trna_weight=args.trna_weight,
-            mrna_weight=args.mrna_weight,
-            scrna_weight=args.scrna_weight,
-            proteomics_weight=args.proteomics_weight,
-            expression_requirement=args.expression_requirement,
-            adjust_method=args.adjustment_method,
-            no_high_confidence=args.no_high_confidence,
-            no_na=args.no_na,
-            merge_zfpkm_distribution=args.merge_zfpkm_distribution,
-            keep_transcriptomics_score=args.keep_transcriptomics_scores,
-        )
+    await _process(
+        context_name=context_name,
+        input_matrices=input_matrices,
+        boolean_matrices=boolean_matrices,
+        source_weights=source_weights,
+        batch_names=batch_names,
+        taxon_id=taxon_id,
+        minimum_source_expression=minimum_source_expression,
+        expression_requirement=expression_requirement,
+        weighted_z_floor=weighted_z_floor,
+        weighted_z_ceiling=weighted_z_ceiling,
+        adjust_method=adjust_method,
+        merge_zfpkm_distribution=merge_zfpkm_distribution,
+        force_activate_high_confidence=force_activate_high_confidence,
+        adjust_for_missing_sources=adjust_for_na,
+        output_activity_filepaths=output_activity_filepaths,
+        output_merge_activity_filepath=output_merge_activity_filepath,
+        output_transcriptomic_details_filepath=output_transcriptomic_details_filepath,
+        output_final_model_scores_filepath=output_final_model_scores_filepath,
+        output_figure_dirpath=output_figure_dirpath,
     )
-
-    logger.success("Finished merging!")
diff --git a/main/como/proteomics/FTPManager.py b/main/como/proteomics/FTPManager.py
index f846481d..b3b96052 100644
--- a/main/como/proteomics/FTPManager.py
+++ b/main/como/proteomics/FTPManager.py
@@ -14,8 +14,11 @@
 from urllib.parse import urlparse
 
 import aioftp
+from loguru import logger
 
-from .FileInformation import FileInformation, clear_print
+from como.proteomics.FileInformation import FileInformation, clear_print
+from como.utils import _log_and_raise_error
+from como.data_types import LogLevel
 
 
 async def aioftp_client(host: str, username: str = "anonymous", password: str = "guest", port: int = 21, max_attempts: int = 3) -> aioftp.Client:
@@ -40,8 +43,11 @@ async def aioftp_client(host: str, username: str = "anonymous", password: str =
             attempt_num += 1
             time.sleep(5)
     if not connection_successful:
-        print()
-        raise ConnectionResetError("Could not connect to FTP server")
+        _log_and_raise_error(
+            "Could not connect to FTP server",
+            error=ConnectionResetError,
+            level=LogLevel.ERROR,
+        )
 
     return client
 
@@ -91,11 +97,19 @@ async def _get_info(self) -> None:
         if url_parse.hostname is not None:
             host = url_parse.hostname
         else:
-            raise ValueError(f"Unable to identify hostname from url: {self._root_link}")
+            _log_and_raise_error(
+                f"Unable to identify hostname from url: {self._root_link}",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
         if url_parse.path != "":
             folder = url_parse.path
         else:
-            raise ValueError(f"Unable to identify folder or path from url: {self._root_link}")
+            _log_and_raise_error(
+                f"Unable to identify folder or path from url: {self._root_link}",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         client = await aioftp_client(host=host)
         for path, info in await client.list(folder, recursive=True):
@@ -170,11 +184,19 @@ async def _aioftp_download_data(self, file_information: FileInformation, semapho
         if url_parse.hostname is not None:
             host = url_parse.hostname
         else:
-            raise ValueError(f"Unable to identify hostname from url: {file_information.download_url}")
+            _log_and_raise_error(
+                f"Unable to identify hostname from url: {file_information.download_url}",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
         if url_parse.path != "":
             folder = url_parse.path
         else:
-            raise ValueError(f"Unable to identify folder or path from url: {file_information.download_url}")
+            _log_and_raise_error(
+                f"Unable to identify folder or path from url: {file_information.download_url}",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         # Convert file size from byte to MB
         size_mb: int = round(file_information.file_size / (1024**2))
diff --git a/main/como/proteomics/proteomics_preprocess.py b/main/como/proteomics/proteomics_preprocess.py
index fe363655..4353a9f6 100644
--- a/main/como/proteomics/proteomics_preprocess.py
+++ b/main/como/proteomics/proteomics_preprocess.py
@@ -7,8 +7,9 @@
 
 from loguru import logger
 
-from . import Crux, FTPManager
-from .FileInformation import FileInformation
+from como.data_types import LogLevel
+from como.proteomics import Crux, FileInformation, FTPManager
+from como.utils import _log_and_raise_error
 
 
 class ArgParseFormatter(argparse.RawTextHelpFormatter, argparse.ArgumentDefaultsHelpFormatter):
@@ -304,12 +305,17 @@ def parse_args() -> argparse.Namespace:
 
     # Validte the input file exists
     if not Path(args.input_csv).is_file():
-        raise FileNotFoundError(f"Input file {args.input} does not exist!")
+        _log_and_raise_error(f"Input file {args.input} does not exist!", error=FileNotFoundError, level=LogLevel.ERROR)
 
     if args.core_count == "all":
         args.core_count = os.cpu_count()
     elif not str(args.core_count).isdigit():
-        raise ValueError(f"Invalid option '{args.core_count}' for option '--cores'. Enter an integer or 'all' to use all cores")
+        _log_and_raise_error(
+            f"Invalid option '{args.core_count}' for option '--cores'. Enter an integer or 'all' to use all cores",
+            error=ValueError,
+            level=LogLevel.ERROR,
+        )
+
     else:
         args.core_count = int(args.core_count)
         if args.core_count > os.cpu_count():
diff --git a/main/como/proteomics_gen.py b/main/como/proteomics_gen.py
index c205aea4..945fa43e 100644
--- a/main/como/proteomics_gen.py
+++ b/main/como/proteomics_gen.py
@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+import sys
+from io import TextIOWrapper
 from pathlib import Path
 
 import numpy as np
@@ -7,9 +9,10 @@
 from fast_bioservices.biodbnet import BioDBNet, Input, Output
 from loguru import logger
 
-from como import return_placeholder_data
+from como.data_types import LOG_FORMAT, LogLevel
 from como.project import Config
 from como.proteomics_preprocessing import protein_transform_main
+from como.utils import _log_and_raise_error, _set_up_logging, return_placeholder_data
 
 
 # Load Proteomics
@@ -18,7 +21,11 @@ def process_proteomics_data(path: Path) -> pd.DataFrame:
     # Preprocess data, drop na, duplicate ';' in symbol,
     matrix: pd.DataFrame = pd.read_csv(path)
     if "gene_symbol" not in matrix.columns:
-        raise ValueError("No gene_symbol column found in proteomics data.")
+        _log_and_raise_error(
+            "No gene_symbol column found in proteomics data.",
+            error=ValueError,
+            level=LogLevel.ERROR,
+        )
 
     matrix["gene_symbol"] = matrix["gene_symbol"].astype(str)
     matrix.dropna(subset=["gene_symbol"], inplace=True)
@@ -127,7 +134,7 @@ def load_empty_dict():
 
     inquiry_full_path = config.data_dir / "config_sheets" / filename
     if not inquiry_full_path.exists():
-        raise FileNotFoundError(f"Error: file not found {inquiry_full_path}")
+        _log_and_raise_error(f"Error: file not found {inquiry_full_path}", error=FileNotFoundError, level=LogLevel.ERROR)
 
     filename = f"Proteomics_{context_name}.csv"
     full_save_filepath = config.result_dir / context_name / "proteomics" / filename
@@ -155,20 +162,44 @@ async def proteomics_gen(
     high_confidence_replicate_ratio: float = 0.7,
     high_confidence_batch_ratio: float = 0.7,
     quantile: int = 25,
+    log_level: LogLevel = LogLevel.INFO,
+    log_location: str | TextIOWrapper = sys.stderr,
 ):
     """Generate proteomics data."""
+    _set_up_logging(level=log_level, location=log_location)
+
     if not config_filepath.exists():
-        raise FileNotFoundError(f"Config file not found at {config_filepath}")
+        _log_and_raise_error(
+            f"Config file not found at {config_filepath}",
+            error=FileNotFoundError,
+            level=LogLevel.ERROR,
+        )
     if config_filepath.suffix not in (".xlsx", ".xls"):
-        raise FileNotFoundError(f"Config file must be an xlsx or xls file at {config_filepath}")
+        _log_and_raise_error(
+            f"Config file must be an xlsx or xls file at {config_filepath}",
+            error=FileNotFoundError,
+            level=LogLevel.ERROR,
+        )
 
     if not matrix_filepath.exists():
-        raise FileNotFoundError(f"Matrix file not found at {matrix_filepath}")
+        _log_and_raise_error(
+            f"Matrix file not found at {matrix_filepath}",
+            error=FileNotFoundError,
+            level=LogLevel.ERROR,
+        )
     if matrix_filepath.suffix not in {".csv"}:
-        raise FileNotFoundError(f"Matrix file must be a csv file at {matrix_filepath}")
+        _log_and_raise_error(
+            f"Matrix file must be a csv file at {matrix_filepath}",
+            error=FileNotFoundError,
+            level=LogLevel.ERROR,
+        )
 
     if quantile < 0 or quantile > 100:
-        raise ValueError("Quantile must be an integer from 0 to 100")
+        _log_and_raise_error(
+            "Quantile must be an integer from 0 to 100",
+            error=ValueError,
+            level=LogLevel.ERROR,
+        )
     quantile /= 100
 
     config_df = pd.read_excel(config_filepath, sheet_name=context_name)
diff --git a/main/como/proteomics_preprocessing.py b/main/como/proteomics_preprocessing.py
index 4093dba0..21f38a78 100644
--- a/main/como/proteomics_preprocessing.py
+++ b/main/como/proteomics_preprocessing.py
@@ -128,7 +128,7 @@ def protein_transform_main(
     output_z_score_matrix_filepath: Path,
 ) -> None:
     """Transform protein abundance data."""
-    abundance_df: pd.DataFrame = pd.read_csv(abundance_df) if isinstance(abundance_df, str | Path) else abundance_df.fillna(0)
+    abundance_df: pd.DataFrame = pd.read_csv(abundance_df) if isinstance(abundance_df, (str, Path)) else abundance_df.fillna(0)
     abundance_df = abundance_df[np.isfinite(abundance_df).all(axis=1)]  # Remove +/- infinity values
     z_transform: ZResult = z_score_calc(abundance_df, min_thresh=0)
 
diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 53da3c69..fbf9f441 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -1,34 +1,34 @@
 from __future__ import annotations
 
-import math
 import multiprocessing
+import sys
 import time
 from collections import namedtuple
 from collections.abc import Callable
+from concurrent.futures import Future, ProcessPoolExecutor, as_completed
 from dataclasses import dataclass, field
 from enum import Enum
-from functools import partial
-from multiprocessing.pool import Pool
+from io import TextIOWrapper
 from pathlib import Path
 from typing import NamedTuple
 
+import matplotlib.pyplot as plt
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
-import plotly.graph_objs as go
-import scanpy as sc
+import seaborn as sns
 import sklearn
 import sklearn.neighbors
-from fast_bioservices.pipeline import ensembl_to_gene_id_and_symbol
+from fast_bioservices.pipeline import ensembl_to_gene_id_and_symbol, gene_symbol_to_ensembl_and_gene_id
 from loguru import logger
 from pandas import DataFrame
-from plotly.subplots import make_subplots
 from scipy.signal import find_peaks
 from sklearn.neighbors import KernelDensity
 
+from como.data_types import FilteringTechnique, LogLevel, PeakIdentificationParameters, RNAType
 from como.migrations import gene_info_migrations
 from como.project import Config
-from como.types import FilteringTechnique, RNAPrepMethod
+from como.utils import _log_and_raise_error, _num_columns, _read_file, _set_up_logging
 
 
 class _FilteringOptions(NamedTuple):
@@ -46,7 +46,7 @@ class LayoutMethod(Enum):
     single_end = "single-end"
 
 
-@dataclass
+@dataclass(slots=True)
 class _StudyMetrics:
     study: str
     num_samples: int
@@ -63,7 +63,11 @@ class _StudyMetrics:
     def __post_init__(self):
         for layout in self.layout:
             if layout not in LayoutMethod:
-                raise ValueError(f"Layout must be 'paired-end' or 'single-end'; got: {layout}")
+                _log_and_raise_error(
+                    f"Layout must be 'paired-end' or 'single-end'; got: {layout}",
+                    error=ValueError,
+                    level=LogLevel.ERROR,
+                )
 
     @property
     def normalization_matrix(self) -> pd.DataFrame:
@@ -133,27 +137,15 @@ def genefilter(data: pd.DataFrame | npt.NDArray, filter_func: Callable[[npt.NDAr
     :return: A NumPy array of the filtered data.
     """
     if not isinstance(data, (pd.DataFrame, npt.NDArray)):
-        raise TypeError("Unsupported data type. Must be a Pandas DataFrame or a NumPy array.")
+        _log_and_raise_error(
+            f"Unsupported data type. Must be a Pandas DataFrame or a NumPy array, got '{type(data)}'",
+            error=TypeError,
+            level=LogLevel.CRITICAL,
+        )
 
     return data.apply(filter_func, axis=1).values if isinstance(data, pd.DataFrame) else np.apply_along_axis(filter_func, axis=1, arr=data)
 
 
-async def _read_counts(path: Path) -> pd.DataFrame:
-    if path.suffix not in {".csv", ".h5ad"}:
-        raise ValueError(f"Unknown file extension '{path.suffix}'. Valid options are '.csv' or '.h5ad'.")
-
-    matrix: pd.DataFrame
-    if path.suffix == ".csv":
-        logger.debug(f"Reading CSV file at '{path}'")
-        matrix = pd.read_csv(path, header=0)
-    elif path.suffix == ".h5ad":
-        logger.debug(f"Reading h5ad file at '{path}'")
-        # Make sample names the columns and gene data the index
-        matrix = sc.read_h5ad(path).to_df().T
-
-    return matrix
-
-
 async def _build_matrix_results(
     *,
     matrix: pd.DataFrame,
@@ -168,9 +160,27 @@ async def _build_matrix_results(
     :param taxon: The NCBI Taxon ID
     :return: A dataclass `ReadMatrixResults`
     """
-    gene_info = gene_info_migrations(gene_info)
+    matrix.dropna(inplace=True)
     conversion = await ensembl_to_gene_id_and_symbol(ids=matrix["ensembl_gene_id"].tolist(), taxon=taxon)
-    matrix = matrix.merge(conversion, on="ensembl_gene_id", how="left")
+    conversion["ensembl_gene_id"] = conversion["ensembl_gene_id"].str.split(",")
+    conversion = conversion.explode("ensembl_gene_id")
+    conversion.reset_index(inplace=True, drop=True)
+
+    merge_on = []
+    if "ensembl_gene_id" in matrix.columns and "ensembl_gene_id" in conversion.columns:
+        merge_on.append("ensembl_gene_id")
+    if "entrez_gene_id" in matrix.columns and "entrez_gene_id" in conversion.columns:
+        merge_on.append("entrez_gene_id")
+    if "gene_symbol" in matrix.columns and "gene_symbol" in conversion.columns:
+        merge_on.append("gene_symbol")
+
+    if not merge_on:
+        _log_and_raise_error(
+            "No columns to merge on. Tested 'ensembl_gene_id', 'entrez_gene_id', and 'gene_symbol'. Please check your input files.",
+            error=ValueError,
+            level=LogLevel.ERROR,
+        )
+    matrix = matrix.merge(conversion, on=merge_on, how="left")
 
     # Only include Entrez and Ensembl Gene IDs that are present in `gene_info`
     matrix["entrez_gene_id"] = matrix["entrez_gene_id"].str.split("//")
@@ -178,6 +188,7 @@ async def _build_matrix_results(
     matrix = matrix.replace(to_replace="-", value=pd.NA).dropna()
     matrix["entrez_gene_id"] = matrix["entrez_gene_id"].astype(int)
 
+    gene_info = gene_info_migrations(gene_info)
     gene_info = gene_info.replace(to_replace="-", value=pd.NA).dropna()
     gene_info["entrez_gene_id"] = gene_info["entrez_gene_id"].astype(int)
 
@@ -186,6 +197,7 @@ async def _build_matrix_results(
         on=["entrez_gene_id", "ensembl_gene_id"],
         how="inner",
     )
+
     gene_info = gene_info.merge(
         counts_matrix[["entrez_gene_id", "ensembl_gene_id"]],
         on=["entrez_gene_id", "ensembl_gene_id"],
@@ -218,58 +230,74 @@ def calculate_tpm(metrics: NamedMetrics) -> NamedMetrics:
     for sample in metrics:
         count_matrix = metrics[sample].count_matrix
 
-        gene_sizes = metrics[sample].gene_sizes
+        gene_sizes = pd.Series(metrics[sample].gene_sizes, index=count_matrix.index)
+        adjusted_counts = count_matrix.add(1e-6)
 
-        tpm_matrix = pd.DataFrame(data=None, index=count_matrix.index, columns=count_matrix.columns)
-        for i in range(len(count_matrix.columns)):
-            values: pd.Series = count_matrix.iloc[:, i] + 1  # Add 1 to prevent division by 0
-            rate = np.log(values.tolist()) - np.log(gene_sizes)
-            denominator = np.log(np.sum(np.exp(rate)))
-            tpm_value = np.exp(rate - denominator + np.log(1e6))
-            tpm_matrix.iloc[:, i] = tpm_value
+        tpm_matrix = adjusted_counts.divide(gene_sizes, axis=0)  # (count + 1) / gene_length
+        tpm_matrix = tpm_matrix.div(tpm_matrix.sum(axis=0), axis=1)  # normalize by total
+        tpm_matrix = tpm_matrix.mul(1e6)  # scale to per-million
         metrics[sample].normalization_matrix = tpm_matrix
 
     return metrics
 
 
-def calculate_fpkm(metrics: NamedMetrics) -> NamedMetrics:
+def _calculate_fpkm(metrics: NamedMetrics, scale: int = 1e6) -> NamedMetrics:
     """Calculate the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) for each sample in the metrics dictionary."""
-    matrix_values = []
     for study in metrics:
+        matrix_values = []
         for sample in range(metrics[study].num_samples):
             layout = metrics[study].layout[sample]
-            count_matrix: npt.NDArray = metrics[study].count_matrix.iloc[:, sample].values
-            gene_size = metrics[study].gene_sizes
-
-            count_matrix = count_matrix.astype(np.float32)
-            gene_size = gene_size.astype(np.float32)
+            count_matrix: pd.DataFrame = metrics[study].count_matrix.iloc[:, sample].values.astype(np.float32)
+            gene_lengths = (
+                metrics[study].fragment_lengths[sample].astype(np.float32)
+                if layout == LayoutMethod.paired_end
+                else metrics[study].gene_sizes.astype(np.float32)
+            )
+            gene_lengths_kb = gene_lengths / 1000.0
 
             match layout:
                 case LayoutMethod.paired_end:  # FPKM
-                    mean_fragment_lengths = metrics[study].fragment_lengths[sample]
-                    # Ensure non-negative value
-                    effective_length = [max(0, size - (mean_fragment_lengths + 1)) for size in gene_size]
-                    n = count_matrix.sum()
-                    fpkm = ((count_matrix + 1) * 1e9) / (np.array(effective_length) * n)
-                    matrix_values.append(fpkm)
+                    total_fragments = count_matrix.sum(axis=0)
+                    if total_fragments == 0:
+                        fragments_per_kilobase_million = np.nan
+                    else:
+                        counts_per_million = total_fragments / scale
+                        fragments_per_kilobase = count_matrix / gene_lengths_kb
+                        fragments_per_kilobase_million = fragments_per_kilobase / counts_per_million
+                    matrix_values.append(fragments_per_kilobase_million)
                 case LayoutMethod.single_end:  # RPKM
-                    # Add a pseudocount before log to ensure log(0) does not happen
-                    rate = np.log(count_matrix + 1) - np.log(gene_size)
-                    exp_rate = np.exp(rate - np.log(np.sum(count_matrix)) + np.log(1e9))
-                    matrix_values.append(exp_rate)
+                    reads_per_kilobase = count_matrix / gene_lengths_kb
+                    total_reads = count_matrix.sum(axis=0)
+                    counts_per_million = total_reads / scale
+                    reads_per_kilobase_million = reads_per_kilobase / counts_per_million
+                    matrix_values.append(reads_per_kilobase_million)
                 case _:
-                    raise ValueError("Invalid normalization method specified")
+                    _log_and_raise_error(
+                        (
+                            f"Invalid normalization method specified ''. "
+                            f"Must be one of '{LayoutMethod.paired_end.value}' or '{LayoutMethod.single_end.value}'."
+                        ),
+                        error=ValueError,
+                        level=LogLevel.ERROR,
+                    )
+
+        # Transpose is needed because values were appended as rows
+        fpkm_matrix = pd.DataFrame(matrix_values).T
+        fpkm_matrix.index = metrics[study].count_matrix.index
+        fpkm_matrix.columns = metrics[study].sample_names
 
-        fpkm_matrix = pd.DataFrame(matrix_values).T  # Transpose is needed because values were appended as rows
         fpkm_matrix = fpkm_matrix[~pd.isna(fpkm_matrix)]
         metrics[study].normalization_matrix = fpkm_matrix
-
         metrics[study].normalization_matrix.columns = metrics[study].count_matrix.columns
 
     return metrics
 
 
-def _zfpkm_calculation(col: pd.Series, kernel: KernelDensity, peak_parameters: tuple[float, float]) -> _ZFPKMResult:
+def _zfpkm_calculation(
+    column: pd.Series,
+    peak_parameters: PeakIdentificationParameters,
+    bandwidth: int = 0.5,
+) -> _ZFPKMResult:
     """Log2 Transformations.
 
     Stabilize the variance in the data to make the distribution more symmetric; this is helpful for Gaussian fitting
@@ -310,17 +338,15 @@ def _zfpkm_calculation(col: pd.Series, kernel: KernelDensity, peak_parameters: t
         - Higher zFPKM values indicate higher expression levels relative to the "inactive" peak
         - A zFPKM value of 0 represents the mean of the "inactive" distribution
         - Research shows that a zFPKM value of -3 or greater can be used as
-            a threshold for calling a gene as "expressed"
+            a threshold for calling a gene as "active" and/or "expressed"
             : https://doi.org/10.1186/1471-2164-14-778
     """
-    col_log2: npt.NDArray = np.log2(col + 1)
-    col_log2 = np.nan_to_num(col_log2, nan=0)
-    refit: KernelDensity = kernel.fit(col_log2.reshape(-1, 1))  # type: ignore
+    values = column.values
+    refit: KernelDensity = KernelDensity(kernel="gaussian", bandwidth=bandwidth).fit(values.reshape(-1, 1))  # type: ignore
 
-    # kde: KernelDensity = KernelDensity(kernel="gaussian", bandwidth=bandwidth).fit(col_log2.reshape(-1, 1))
-    x_range = np.linspace(col_log2.min(), col_log2.max(), 1000)
+    x_range = np.linspace(values.min(), values.max(), 2000)
     density = np.exp(refit.score_samples(x_range.reshape(-1, 1)))
-    peaks, _ = find_peaks(density, height=peak_parameters[0], distance=peak_parameters[1])
+    peaks, _ = find_peaks(density, height=peak_parameters.height, distance=peak_parameters.distance)
     peak_positions = x_range[peaks]
 
     mu = 0
@@ -330,17 +356,17 @@ def _zfpkm_calculation(col: pd.Series, kernel: KernelDensity, peak_parameters: t
     if len(peaks) != 0:
         mu = peak_positions.max()
         max_fpkm = density[peaks[np.argmax(peak_positions)]]
-        u = col_log2[col_log2 > mu].mean()
+        u = values[values > mu].mean()
         stddev = (u - mu) * np.sqrt(np.pi / 2)
-    zfpkm = pd.Series((col_log2 - mu) / stddev, dtype=np.float32, name=col.name)
+    zfpkm = pd.Series((values - mu) / stddev, dtype=np.float32, name=column.name)
 
     return _ZFPKMResult(zfpkm=zfpkm, density=Density(x_range, density), mu=mu, std_dev=stddev, max_fpkm=max_fpkm)
 
 
 def zfpkm_transform(
     fpkm_df: pd.DataFrame,
-    bandwidth: int = 0.5,
-    peak_parameters: tuple[float, float] = (0.02, 1.0),
+    peak_parameters: PeakIdentificationParameters,
+    bandwidth: int,
     update_every_percent: float = 0.1,
 ) -> tuple[dict[str, _ZFPKMResult], DataFrame]:
     """Perform zFPKM calculation/transformation."""
@@ -348,56 +374,62 @@ def zfpkm_transform(
         logger.warning(f"update_every_percent should be a decimal value between 0 and 1; got: {update_every_percent} - will convert to percentage")
         update_every_percent /= 100
 
-    total = len(fpkm_df.columns)
-    update_per_step: int = int(np.ceil(total * update_every_percent))
-    cores = min(multiprocessing.cpu_count() - 2, total)
-    logger.debug(f"Processing {total:,} samples through zFPKM transform using {cores} cores")
-    logger.debug(f"Will update every {update_per_step:,} steps as this is approximately {update_every_percent:.1%} of {total:,}")
-
-    with Pool(processes=cores) as pool:
-        kernel = KernelDensity(kernel="gaussian", bandwidth=bandwidth)
-        chunksize = math.ceil(len(fpkm_df.columns) / (4 * cores))
-        partial_func = partial(_zfpkm_calculation, kernel=kernel, peak_parameters=peak_parameters)
-        chunk_time = time.time()
-        start_time = time.time()
-
-        log_padding = len(str(f"{total:,}"))
-        zfpkm_df = pd.DataFrame(data=0, index=fpkm_df.index, columns=fpkm_df.columns)
-        results: dict[str, _ZFPKMResult] = {}
-        result: _ZFPKMResult
-        for i, result in enumerate(
-            pool.imap(
-                partial_func,
-                (fpkm_df[col] for col in fpkm_df.columns),
-                chunksize=chunksize,
+    total_samples = _num_columns(fpkm_df)
+    update_per_step: int = int(np.ceil(total_samples * update_every_percent))
+
+    # Get at least 1 core and at most cpu_count() - 2
+    cores = max(min(multiprocessing.cpu_count() - 2, total_samples), 1)
+    logger.debug(f"zFPKM transforming {len(fpkm_df.columns)} sample(s) containing {len(fpkm_df):,} genes(s) using {cores} core(s)")
+    logger.debug(f"Will update every {update_per_step:,} steps (~{update_every_percent:.1%} of {total_samples:,})")
+
+    chunk_time = time.time()
+    start_time = time.time()
+    log_padding = len(str(f"{total_samples:,}"))
+    zfpkm_series: list[pd.Series] = []
+    results: dict[str, _ZFPKMResult] = {}
+
+    with ProcessPoolExecutor(max_workers=cores) as pool:
+        futures: list[Future[_ZFPKMResult]] = [
+            pool.submit(
+                _zfpkm_calculation,
+                column=fpkm_df[column],
+                peak_parameters=peak_parameters,
+                bandwidth=bandwidth,
             )
-        ):
+            for column in fpkm_df
+        ]
+        for i, future in enumerate(as_completed(futures)):
+            result = future.result()
             key = str(result.zfpkm.name)
             results[key] = result
-            zfpkm_df[key] = result.zfpkm
+            zfpkm_series.append(result.zfpkm)
 
-            # show updates every X% and at the end, but skip on first iteration
-            if i != 0 and (i % update_per_step == 0 or i == total):
+            if i != 0 and ((i + 1) % update_per_step == 0 or (i + 1) == total_samples):
                 current_time = time.time()
                 chunk = current_time - chunk_time
                 total_time = current_time - start_time
-                formatted = f"{i:,}"
+                chunk_num = f"{i + 1:,}"
                 logger.debug(
-                    f"Processed {formatted:>{log_padding}} of {total:,} - chunk took {chunk:.1f} seconds - running for {total_time:.1f} seconds"
+                    f"Processed {chunk_num:>{log_padding}} of {total_samples:,} - "
+                    f"chunk took {chunk:.1f} seconds - "
+                    f"running for {total_time:.1f} seconds"
                 )
                 chunk_time = current_time
+
+    zfpkm_df = pd.DataFrame({series.name: series for series in zfpkm_series}, index=fpkm_df.index)
     return results, zfpkm_df
 
 
-def zfpkm_plot(results, *, plot_xfloor: int = -4, subplot_titles: bool = True):
+def zfpkm_plot(results, *, output_png_filepath: Path, plot_xfloor: int = -4):
     """Plot the log2(FPKM) density and fitted Gaussian for each sample.
 
     :param results: A dictionary of intermediate results from zfpkm_transform.
+    :param output_png_filepath: Output filepath location
     :param: subplot_titles: Whether to display facet titles (sample names).
     :param plot_xfloor: Lower limit for the x-axis.
     :param subplot_titles: Whether to display facet titles (sample names).
     """
-    mega_df = pd.DataFrame(columns=["sample_name", "log2fpkm", "fpkm_density", "fitted_density_scaled"])
+    to_concat: list[pd.DataFrame] = [None] * len(results)  # type: ignore  # ignoring because None is not of type pd.DataFrame
     for name, result in results.items():
         stddev = result.std_dev
         x = np.array(result.density.x)
@@ -408,37 +440,42 @@ def zfpkm_plot(results, *, plot_xfloor: int = -4, subplot_titles: bool = True):
         max_fitted = fitted.max()
         scale_fitted = fitted * (max_fpkm / max_fitted)
 
-        df = pd.DataFrame(
-            {
-                "sample_name": [name] * len(x),
-                "log2fpkm": x,
-                "fpkm_density": y,
-                "fitted_density_scaled": scale_fitted,
-            }
+        to_concat.append(
+            pd.DataFrame(
+                {
+                    "sample_name": [name] * len(x),
+                    "log2fpkm": x,
+                    "fpkm_density": y,
+                    "fitted_density_scaled": scale_fitted,
+                }
+            )
         )
-        mega_df = pd.concat([mega_df, df], ignore_index=True)
-
+    mega_df = pd.concat(to_concat, ignore_index=True)
+    mega_df.columns = pd.Series(data=["sample_name", "log2fpkm", "fpkm_density", "fitted_density_scaled"])
     mega_df = mega_df.melt(id_vars=["log2fpkm", "sample_name"], var_name="source", value_name="density")
-    subplot_titles = list(results.keys()) if subplot_titles else None
-    fig = make_subplots(
-        rows=len(results),
-        cols=1,
-        subplot_titles=subplot_titles,
-        vertical_spacing=min(0.05, (1 / (len(results) - 1))),
-    )
 
-    for i, (name, group) in enumerate(mega_df.groupby("sample_name"), start=1):
-        fig.add_trace(
-            trace=go.Scatter(x=group["log2fpkm"], y=group["density"], mode="lines", name=name, legendgroup=name),
-            row=i,
-            col=1,
-        )
-        fig.update_xaxes(title_text="log2(FPKM)", range=[plot_xfloor, max(group["log2fpkm"].tolist())], row=i, col=1)
-        fig.update_yaxes(title_text="density [scaled]", row=i, col=1)
-        fig.update_layout(legend_tracegroupgap=0)
+    _, axes = plt.subplots(nrows=len(results), ncols=1, figsize=(8, 4 * len(results)))
+    if len(results) == 1:
+        axes = [axes]
+
+    for i, sample_name in enumerate(results):
+        sample_data = mega_df[mega_df["sample_name"] == sample_name]
+        axis = axes[i]
+
+        for source_type in sample_data["source"].unique():
+            group = sample_data[sample_data["source"] == source_type]
+            sns.lineplot(data=group, x="log2fpkm", y="density", label=source_type, ax=axis)
+
+        axis.set_xlim(plot_xfloor, sample_data["log2fpkm"].max())
+        axis.set_xlabel("log2(FPKM)")
+        axis.set_ylabel("density [scaled]")
+        axis.legend(title="Source")
 
-    fig.update_layout(height=600 * len(results), width=1000, title_text="zFPKM Plots", showlegend=True)
-    fig.write_image("zfpkm_plot.png")
+    plt.tight_layout()
+    if output_png_filepath.suffix != ".png":
+        logger.warning(f"Output filepath did not end in '.png', setting to '.png' now. Got: '{output_png_filepath.suffix}'")
+        output_png_filepath = output_png_filepath.with_suffix(".png")
+    plt.savefig(output_png_filepath)
 
 
 def calculate_z_score(metrics: NamedMetrics) -> NamedMetrics:
@@ -455,7 +492,7 @@ def cpm_filter(
     context_name: str,
     metrics: NamedMetrics,
     filtering_options: _FilteringOptions,
-    prep: RNAPrepMethod,
+    prep: RNAType,
 ) -> NamedMetrics:
     """Apply Counts Per Million (CPM) filtering to the count matrix for a given sample."""
     config = Config()
@@ -480,6 +517,7 @@ def cpm_filter(
         counts_per_million: pd.DataFrame = (counts / library_size) * 1_000_000
         counts_per_million.insert(0, "entrez_gene_ids", pd.Series(entrez_ids))
         logger.debug(f"Writing CPM matrix to {output_filepath}")
+        counts_per_million.dropna(inplace=True)
         counts_per_million.to_csv(output_filepath, index=False)
 
         # TODO: Counts per million is adding ~61,500 columns (equal to the number of genes) for some reason.
@@ -526,7 +564,7 @@ def tpm_quantile_filter(*, metrics: NamedMetrics, filtering_options: _FilteringO
 
         # Only keep `entrez_gene_ids` that pass `min_genes`
         metric.entrez_gene_ids = [gene for gene, keep in zip(entrez_ids, min_genes, strict=True) if keep]
-        metric.gene_sizes = [gene for gene, keep in zip(gene_size, min_genes, strict=True) if keep]
+        metric.gene_sizes = np.array(gene for gene, keep in zip(gene_size, min_genes, strict=True) if keep)
         metric.count_matrix = metric.count_matrix.iloc[min_genes, :]
         metric.normalization_matrix = metrics[sample].normalization_matrix.iloc[min_genes, :]
 
@@ -538,25 +576,44 @@ def tpm_quantile_filter(*, metrics: NamedMetrics, filtering_options: _FilteringO
     return metrics
 
 
-def zfpkm_filter(*, metrics: NamedMetrics, filtering_options: _FilteringOptions, calcualte_fpkm: bool) -> NamedMetrics:
+def zfpkm_filter(
+    *,
+    metrics: NamedMetrics,
+    filtering_options: _FilteringOptions,
+    calculate_fpkm: bool,
+    force_zfpkm_plot: bool,
+    peak_parameters: PeakIdentificationParameters,
+    bandwidth: int,
+    output_png_filepath: Path | None,
+) -> NamedMetrics:
     """Apply zFPKM filtering to the FPKM matrix for a given sample."""
     min_sample_expression = filtering_options.replicate_ratio
     high_confidence_sample_expression = filtering_options.high_replicate_ratio
     cut_off = filtering_options.cut_off
+    metrics = _calculate_fpkm(metrics) if calculate_fpkm else metrics
 
-    if calcualte_fpkm:
-        metrics = calculate_fpkm(metrics)
-
-    metric: _StudyMetrics
     for metric in metrics.values():
+        metric: _StudyMetrics
         # if fpkm was not calculated, the normalization matrix will be empty; collect the count matrix instead
         matrix = metric.count_matrix if metric.normalization_matrix.empty else metric.normalization_matrix
-        matrix = matrix[matrix.sum(axis=1) > 0]
+        matrix = matrix[matrix.sum(axis=1) > 0]  # remove rows (genes) that have no counts across all samples
+
+        results, zfpkm_df = zfpkm_transform(matrix, peak_parameters=peak_parameters, bandwidth=bandwidth)
+        zfpkm_df[(matrix == 0) | (zfpkm_df.isna())] = -4
 
-        minimums = matrix == 0
-        results, zfpkm_df = zfpkm_transform(matrix)
-        zfpkm_df[minimums] = -4
-        zfpkm_plot(results)
+        if len(results) > 10 and not force_zfpkm_plot:
+            logger.warning(
+                "Not plotting zFPKM results because more than 10 plots would be created. "
+                "If you would like to plot them anyway, set 'force_zfpkm_plot' to True"
+            )
+        elif output_png_filepath is None:
+            logger.critical("Output zFPKM PNG filepath is None, set a path to plot zFPKM graphs")
+        else:
+            output_png_filepath.parent.mkdir(parents=True, exist_ok=True)
+            output_png_filepath.unlink(missing_ok=True)
+            zfpkm_plot(results, output_png_filepath=output_png_filepath)
+
+        metric.z_score_matrix = zfpkm_df
 
         # determine which genes are expressed
         min_samples = round(min_sample_expression * len(zfpkm_df.columns))
@@ -579,29 +636,53 @@ def filter_counts(
     metrics: NamedMetrics,
     technique: FilteringTechnique,
     filtering_options: _FilteringOptions,
-    prep: RNAPrepMethod,
+    prep: RNAType,
+    force_zfpkm_plot: bool,
+    peak_parameters: PeakIdentificationParameters,
+    bandwidth: int,
+    output_png_filepath: Path | None = None,
 ) -> NamedMetrics:
     """Filter the count matrix based on the specified technique."""
     match technique:
-        case FilteringTechnique.cpm:
+        case FilteringTechnique.CPM:
             return cpm_filter(context_name=context_name, metrics=metrics, filtering_options=filtering_options, prep=prep)
-        case FilteringTechnique.tpm:
+        case FilteringTechnique.TPM:
             return tpm_quantile_filter(metrics=metrics, filtering_options=filtering_options)
-        case FilteringTechnique.zfpkm:
-            return zfpkm_filter(metrics=metrics, filtering_options=filtering_options, calcualte_fpkm=True)
-        case FilteringTechnique.umi:
-            return zfpkm_filter(metrics=metrics, filtering_options=filtering_options, calcualte_fpkm=False)
+        case FilteringTechnique.ZFPKM:
+            return zfpkm_filter(
+                metrics=metrics,
+                filtering_options=filtering_options,
+                calculate_fpkm=True,
+                force_zfpkm_plot=force_zfpkm_plot,
+                peak_parameters=peak_parameters,
+                bandwidth=bandwidth,
+                output_png_filepath=output_png_filepath,
+            )
+        case FilteringTechnique.UMI:
+            # UMI filtering is the same as zFPKM filtering without calculating FPKM
+            return zfpkm_filter(
+                metrics=metrics,
+                filtering_options=filtering_options,
+                calculate_fpkm=False,
+                force_zfpkm_plot=force_zfpkm_plot,
+                peak_parameters=peak_parameters,
+                bandwidth=bandwidth,
+                output_png_filepath=output_png_filepath,
+            )
         case _:
-            raise ValueError(f"Technique must be one of {FilteringTechnique}")
+            _log_and_raise_error(
+                f"Technique must be one of {FilteringTechnique}, got '{technique.value}'",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
 
-async def _save_rnaseq_tests(
+async def _process(
     context_name: str,
-    rnaseq_matrix: pd.DataFrame,
+    rnaseq_matrix_filepath: Path,
     metadata_df: pd.DataFrame,
     gene_info_df: pd.DataFrame,
-    output_filepath: Path,
-    prep: RNAPrepMethod,
+    prep: RNAType,
     taxon: int,
     replicate_ratio: float,
     batch_ratio: float,
@@ -609,8 +690,23 @@ async def _save_rnaseq_tests(
     high_batch_ratio: float,
     technique: FilteringTechnique,
     cut_off: int | float,
+    force_zfpkm_plot: bool,
+    peak_parameters: PeakIdentificationParameters,
+    bandwidth: int,
+    output_boolean_activity_filepath: Path,
+    output_zscore_normalization_filepath: Path,
+    output_zfpkm_png_filepath: Path | None,
 ):
     """Save the results of the RNA-Seq tests to a CSV file."""
+    output_boolean_activity_filepath.parent.mkdir(parents=True, exist_ok=True)
+
+    rnaseq_matrix: pd.DataFrame = await _read_file(rnaseq_matrix_filepath)
+    if rnaseq_matrix_filepath.suffix == ".h5ad":
+        conversion = await gene_symbol_to_ensembl_and_gene_id(symbols=rnaseq_matrix["gene_symbol"].tolist(), taxon=taxon)
+        conversion.reset_index(inplace=True)
+        rnaseq_matrix = rnaseq_matrix.merge(conversion, how="left", on="gene_symbol")
+        rnaseq_matrix.replace(to_replace=pd.NA, value="-")
+
     filtering_options = _FilteringOptions(
         replicate_ratio=replicate_ratio,
         batch_ratio=batch_ratio,
@@ -625,23 +721,50 @@ async def _save_rnaseq_tests(
         metadata_df=metadata_df,
         taxon=taxon,
     )
+
     metrics = read_counts_results.metrics
     entrez_gene_ids = read_counts_results.entrez_gene_ids
 
-    metrics = filter_counts(
+    metrics: NamedMetrics = filter_counts(
         context_name=context_name,
         metrics=metrics,
         technique=technique,
         filtering_options=filtering_options,
         prep=prep,
+        force_zfpkm_plot=force_zfpkm_plot,
+        peak_parameters=peak_parameters,
+        bandwidth=bandwidth,
+        output_png_filepath=output_zfpkm_png_filepath,
     )
 
+    merged_zscore_df = pd.DataFrame()
     expressed_genes: list[str] = []
     top_genes: list[str] = []
     for metric in metrics.values():
         expressed_genes.extend(metric.entrez_gene_ids)
         top_genes.extend(metric.high_confidence_entrez_gene_ids)
 
+        merged_zscore_df = (
+            metric.z_score_matrix
+            if merged_zscore_df.empty
+            else merged_zscore_df.merge(
+                metric.z_score_matrix,
+                how="outer",
+                left_index=True,
+                right_index=True,
+            )
+        )
+    merged_zscore_df[merged_zscore_df.isna()] = -4
+
+    # If any of the normalization metrics are not empty, write the normalized metrics to disk
+    if not all(metric.normalization_matrix.empty for metric in metrics.values()):
+        merged_zscore_df.to_csv(output_zscore_normalization_filepath, index=True)
+        logger.success(f"Wrote z-score normalization matrix to {output_zscore_normalization_filepath}")
+    else:
+        logger.warning(
+            "Not writing z-score normalization matrix because no normalization matrices exist. This is expected if you are using UMI filtering."
+        )
+
     expression_frequency = pd.Series(expressed_genes).value_counts()
     expression_df = pd.DataFrame({"entrez_gene_id": expression_frequency.index, "frequency": expression_frequency.values})
     expression_df["prop"] = expression_df["frequency"] / len(metrics)
@@ -662,32 +785,34 @@ async def _save_rnaseq_tests(
     expressed_count = len(boolean_matrix[boolean_matrix["expressed"] == 1])
     high_confidence_count = len(boolean_matrix[boolean_matrix["high"] == 1])
 
-    boolean_matrix.to_csv(output_filepath, index=False)
-    logger.info(f"{context_name} - Found {expressed_count} expressed and {high_confidence_count} confidently expressed genes")
-    logger.success(f"Wrote boolean matrix to {output_filepath}")
-
+    boolean_matrix.dropna(subset="entrez_gene_id", inplace=True)
+    boolean_matrix.to_csv(output_boolean_activity_filepath, index=False)
+    logger.info(f"{context_name} - Found {expressed_count} expressed genes, {high_confidence_count} of which are confidently expressed")
+    logger.success(f"Wrote boolean matrix to {output_boolean_activity_filepath}")
 
-async def _create_metadata_df(path: Path) -> pd.DataFrame:
-    if path.suffix not in {".xls", ".xlsx"}:
-        raise ValueError(f"Expected an excel file with extension of '.xlsx' or '.xls', got '{path.suffix}'. Attempted to process: {path}")
-    return pd.read_excel(path)
 
-
-async def rnaseq_gen(  # noqa: C901, allow complex function
+async def rnaseq_gen(  # noqa: C901
     context_name: str,
     input_rnaseq_filepath: Path,
     input_gene_info_filepath: Path,
-    output_rnaseq_filepath: Path,
-    prep: RNAPrepMethod,
-    taxon: int,
-    input_metadata_filepath: Path | None = None,
-    input_metadata_df: pd.DataFrame | None = None,
+    prep: RNAType,
+    taxon_id: int,
+    output_boolean_activity_filepath: Path,
+    output_zscore_normalization_filepath: Path,
+    input_metadata_filepath_or_df: Path | pd.DataFrame,
     replicate_ratio: float = 0.5,
     high_replicate_ratio: float = 1.0,
     batch_ratio: float = 0.5,
     high_batch_ratio: float = 1.0,
-    technique: FilteringTechnique | str = FilteringTechnique.tpm,
+    technique: FilteringTechnique | str = FilteringTechnique.ZFPKM,
+    zfpkm_peak_height: float = 0.02,
+    zfpkm_peak_distance: float = 1.0,
+    zfpkm_bandwidth: int = 1,
     cutoff: int | float | None = None,
+    force_zfpkm_plot: bool = False,
+    log_level: LogLevel = LogLevel.INFO,
+    log_location: str | TextIOWrapper = sys.stderr,
+    output_zfpkm_png_filepath: Path | None = None,
 ) -> None:
     """Generate a list of active and high-confidence genes from a gene count matrix.
 
@@ -698,11 +823,11 @@ async def rnaseq_gen(  # noqa: C901, allow complex function
     :param context_name: The name of the context being processed
     :param input_rnaseq_filepath: The filepath to the gene count matrix
     :param input_gene_info_filepath: The filepath to the gene info file
-    :param output_rnaseq_filepath: The filepath to write the output gene count matrix
+    :param output_boolean_activity_filepath: The filepath to write the output gene count matrix
+    :param output_zscore_normalization_filepath: The filepath to write the output z-score normalization matrix
     :param prep: The preparation method
-    :param taxon: The NCBI Taxon ID
-    :param input_metadata_filepath: The filepath to the metadata file
-    :param input_metadata_df: The metadata dataframe
+    :param taxon_id: The NCBI Taxon ID
+    :param input_metadata_filepath_or_df: The filepath or dataframe containing metadata information
     :param replicate_ratio: The percentage of replicates that a gene must
         appear in for a gene to be marked as "active" in a batch/study
     :param batch_ratio: The percentage of batches that a gene must appear in for a gene to be marked as 'active"
@@ -711,59 +836,105 @@ async def rnaseq_gen(  # noqa: C901, allow complex function
     :param high_batch_ratio: The percentage of batches that a gene must
         appear in for a gene to be marked "highly confident" in its expression
     :param technique: The filtering technique to use
+    :param zfpkm_peak_height: The height of the zFPKM peak
+    :param zfpkm_peak_distance: The distance of the zFPKM peak
+    :param zfpkm_bandwidth: The bandwidth of the zFPKM
     :param cutoff: The cutoff value to use for the provided filtering technique
+    :param force_zfpkm_plot: If too many samples exist, should plotting be done anyway?
+    :param log_level: The level of logging to output
+    :param log_location: The location to write logs to
+    :param output_zfpkm_png_filepath: Optional filepath to save zFPKM plots
     :return: None
     """
-    if not input_metadata_df and not input_metadata_filepath:
-        raise ValueError("At least one of input_metadata_filepath or input_metadata_df must be provided")
-
-    technique = FilteringTechnique.from_string(str(technique.lower())) if isinstance(technique, (str, int)) else technique
+    _set_up_logging(level=log_level, location=log_location)
 
+    technique = FilteringTechnique(technique) if isinstance(technique, str) else technique
     match technique:
-        case FilteringTechnique.tpm:
+        case FilteringTechnique.TPM:
             cutoff = cutoff or 25
             if cutoff < 1 or cutoff > 100:
-                raise ValueError("Quantile must be between 1 - 100")
+                _log_and_raise_error(
+                    "Quantile must be between 1 - 100",
+                    error=ValueError,
+                    level=LogLevel.ERROR,
+                )
 
-        case FilteringTechnique.cpm:
+        case FilteringTechnique.CPM:
             if cutoff and cutoff < 0:
-                raise ValueError("Cutoff must be greater than 0")
+                _log_and_raise_error(
+                    "Cutoff must be greater than or equal to 0",
+                    error=ValueError,
+                    level=LogLevel.ERROR,
+                )
             elif cutoff:
                 cutoff = "default"
 
-        case FilteringTechnique.zfpkm:
+        case FilteringTechnique.ZFPKM | FilteringTechnique.UMI:
             cutoff = cutoff or -3
-        case FilteringTechnique.umi:
-            pass
         case _:
-            raise ValueError(f"Technique must be one of {FilteringTechnique}")
+            _log_and_raise_error(
+                f"Technique must be one of {','.join(FilteringTechnique)}. Got: {technique.value}",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
     if not input_rnaseq_filepath.exists():
-        raise FileNotFoundError(f"Input RNA-seq file not found! Searching for: '{input_rnaseq_filepath}'")
+        _log_and_raise_error(
+            f"Input RNA-seq file not found! Searching for: '{input_rnaseq_filepath}'",
+            error=FileNotFoundError,
+            level=LogLevel.ERROR,
+        )
 
-    if prep == RNAPrepMethod.SCRNA:
-        technique = FilteringTechnique.umi
+    if prep == RNAType.SCRNA and technique.value.lower() != FilteringTechnique.UMI.value.lower():
         logger.warning(
             "Single cell filtration does not normalize and assumes "
-            "gene counts are counted with Unique Molecular Identifiers (UMIs). "
-            "Setting filtering technique to UMI now."
+            "genes are counted with Unique Molecular Identifiers (UMIs). "
+            f"Switching filtering technique from '{technique.value}' to '{FilteringTechnique.UMI.value}'."
         )
+        technique = FilteringTechnique.UMI
+
+    if isinstance(input_metadata_filepath_or_df, pd.DataFrame):
+        metadata_df = input_metadata_filepath_or_df
+    elif isinstance(input_metadata_filepath_or_df, Path):
+        if input_metadata_filepath_or_df.suffix not in {".xlsx", ".xls"}:
+            _log_and_raise_error(
+                f"Expected an excel file with extension of '.xlsx' or '.xls', got '{input_metadata_filepath_or_df.suffix}'.",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
+        if not input_metadata_filepath_or_df.exists():
+            _log_and_raise_error(
+                f"Input metadata file not found! Searching for: '{input_metadata_filepath_or_df}'",
+                error=FileNotFoundError,
+                level=LogLevel.ERROR,
+            )
 
-    logger.debug(f"Starting '{context_name}'")
-    output_rnaseq_filepath.parent.mkdir(parents=True, exist_ok=True)
+        metadata_df = pd.read_excel(input_metadata_filepath_or_df)
+    else:
+        _log_and_raise_error(
+            f"Expected a pandas DataFrame or Path object as metadata, got '{type(input_metadata_filepath_or_df)}'",
+            error=TypeError,
+            level=LogLevel.ERROR,
+        )
 
-    await _save_rnaseq_tests(
+    logger.debug(f"Starting '{context_name}'")
+    await _process(
         context_name=context_name,
-        rnaseq_matrix=await _read_counts(input_rnaseq_filepath),
-        metadata_df=input_metadata_df or await _create_metadata_df(input_metadata_filepath),
-        gene_info_df=pd.read_csv(input_gene_info_filepath),
-        output_filepath=output_rnaseq_filepath,
+        rnaseq_matrix_filepath=input_rnaseq_filepath,
+        metadata_df=metadata_df,
+        gene_info_df=await _read_file(input_gene_info_filepath),
         prep=prep,
-        taxon=taxon,
+        taxon=taxon_id,
         replicate_ratio=replicate_ratio,
         batch_ratio=batch_ratio,
         high_replicate_ratio=high_replicate_ratio,
         high_batch_ratio=high_batch_ratio,
         technique=technique,
         cut_off=cutoff,
+        force_zfpkm_plot=force_zfpkm_plot,
+        peak_parameters=PeakIdentificationParameters(height=zfpkm_peak_height, distance=zfpkm_peak_distance),
+        bandwidth=zfpkm_bandwidth,
+        output_boolean_activity_filepath=output_boolean_activity_filepath,
+        output_zscore_normalization_filepath=output_zscore_normalization_filepath,
+        output_zfpkm_png_filepath=output_zfpkm_png_filepath,
     )
diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index dac056fe..41b8d971 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -1,25 +1,24 @@
 from __future__ import annotations
 
 import asyncio
-import contextlib
+import json
 import re
 import sys
-from dataclasses import dataclass, field
+from dataclasses import asdict, dataclass, field
 from io import StringIO, TextIOWrapper
 from itertools import chain
 from pathlib import Path
-from typing import Literal
+from typing import Final, Literal
 
 import aiofiles
 import numpy as np
 import pandas as pd
-import scanpy as sc
 from fast_bioservices.biothings.mygene import MyGene
 from fast_bioservices.pipeline import ensembl_to_gene_id_and_symbol, gene_symbol_to_ensembl_and_gene_id
 from loguru import logger
 
-from como.types import RNAPrepMethod, type_path, type_rna
-from como.utils import _listify
+from como.data_types import PATH_TYPE, LogLevel, RNAType
+from como.utils import _listify, _log_and_raise_error, _read_file, _set_up_logging
 
 
 @dataclass
@@ -38,22 +37,31 @@ def num_genes(self) -> int:
     @classmethod
     async def build_from_tab(cls, filepath: Path) -> _STARinformation:
         if filepath.suffix != ".tab":
-            raise ValueError(f"Building STAR information requires a '.tab' file; received: '{filepath}'")
+            _log_and_raise_error(
+                f"Building STAR information requires a '.tab' file; received: '{filepath}'",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
+        if not filepath.exists():
+            _log_and_raise_error(
+                f"Unable to find the .tab file '{filepath}'",
+                error=FileNotFoundError,
+                level=LogLevel.ERROR,
+            )
 
-        async with aiofiles.open(filepath) as i_stream:
-            unmapped = await i_stream.readline()
-            multimapping = await i_stream.readline()
-            no_feature = await i_stream.readline()
-            ambiguous = await i_stream.readline()
+        with filepath.open("r") as i_stream:
+            unmapped = i_stream.readline()
+            multimapping = i_stream.readline()
+            no_feature = i_stream.readline()
+            ambiguous = i_stream.readline()
 
             num_unmapped = [int(i) for i in unmapped.rstrip("\n").split("\t")[1:]]
             num_multimapping = [int(i) for i in multimapping.rstrip("\n").split("\t")[1:]]
             num_no_feature = [int(i) for i in no_feature.rstrip("\n").split("\t")[1:]]
             num_ambiguous = [int(i) for i in ambiguous.rstrip("\n").split("\t")[1:]]
-            remainder = await i_stream.read()
+            remainder = await asyncio.to_thread(i_stream.read)
 
-        string_io = StringIO(remainder)
-        df = pd.read_csv(string_io, sep="\t", header=None)
+        df = await _read_file(StringIO(remainder), sep="\t", header=None)
         df.columns = [
             "ensembl_gene_id",
             "unstranded_rna_counts",
@@ -92,35 +100,82 @@ def __post_init__(self):
         self.__sample_names = [f.stem for f in self.count_files]
 
         if len(self.count_files) != len(self.strand_files):
-            raise ValueError(
-                f"Unequal number of count files and strand files for study '{self.study_name}'. "
-                f"Found {len(self.count_files)} count files and {len(self.strand_files)} strand files."
+            _log_and_raise_error(
+                (
+                    f"Unequal number of count files and strand files for study '{self.study_name}'. "
+                    f"Found {len(self.count_files)} count files and {len(self.strand_files)} strand files."
+                ),
+                error=ValueError,
+                level=LogLevel.ERROR,
             )
 
         if self.num_samples != len(self.count_files):
-            raise ValueError(
-                f"Unequal number of samples and count files for study '{self.study_name}'. "
-                f"Found {self.num_samples} samples and {len(self.count_files)} count files."
+            _log_and_raise_error(
+                (
+                    f"Unequal number of samples and count files for study '{self.study_name}'. "
+                    f"Found {self.num_samples} samples and {len(self.count_files)} count files."
+                ),
+                error=ValueError,
+                level=LogLevel.ERROR,
             )
 
         if self.num_samples != len(self.strand_files):
-            raise ValueError(
-                f"Unequal number of samples and strand files for study '{self.study_name}'. "
-                f"Found {self.num_samples} samples and {len(self.strand_files)} strand files."
+            _log_and_raise_error(
+                (
+                    f"Unequal number of samples and strand files for study '{self.study_name}'. "
+                    f"Found {self.num_samples} samples and {len(self.strand_files)} strand files."
+                ),
+                error=ValueError,
+                level=LogLevel.ERROR,
             )
 
         if self.__num_samples == 1:
-            raise ValueError(f"Only one sample exists for study {self.study_name}. Provide at least two samples")
+            _log_and_raise_error(
+                f"Only one sample exists for study {self.study_name}. Provide at least two samples",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         self.count_files.sort()
         self.strand_files.sort()
         self.__sample_names.sort()
 
 
+@dataclass(slots=True)
+class SampleConfiguration:
+    sample_name: str
+    fragment_length: float
+    layout: str
+    strand: str
+    study: str
+    library_prep: str
+
+
+async def _read_text(path: Path | None, *, default: str, lower: bool = False) -> str:
+    if path is None:
+        return default
+    async with aiofiles.open(path) as f:
+        txt = (await f.read()).strip()
+    return txt.lower() if lower else txt
+
+
 def _sample_name_from_filepath(file: Path) -> str:
     return re.search(r".+_S\d+R\d+", file.stem).group()
 
 
+def _require_one(paths: list[Path], kind: Literal["layout", "strand", "preparation", "fragment"], label: str) -> Path | None:
+    if len(paths) == 1:
+        return paths[0]
+    if len(paths) == 0:
+        return None
+    _log_and_raise_error(
+        f"Multiple matching {kind} files for {label}, make sure there is only one copy for each replicate in COMO_input",
+        error=ValueError,
+        level=LogLevel.ERROR,
+    )
+    return None  # explicit return None to satisfy type-check
+
+
 def _organize_gene_counts_files(data_dir: Path) -> list[_StudyMetrics]:
     gene_count_dir = Path(data_dir, "geneCounts")
     strand_dir = Path(data_dir, "strandedness")
@@ -129,10 +184,14 @@ def _organize_gene_counts_files(data_dir: Path) -> list[_StudyMetrics]:
     strandedness_directories: list[Path] = sorted([p for p in strand_dir.glob("*") if not p.name.startswith(".")])
 
     if len(gene_counts_directories) != len(strandedness_directories):
-        raise ValueError(
-            f"Unequal number of gene count directories and strandedness directories. "
-            f"Found {len(gene_counts_directories)} gene count directories and {len(strandedness_directories)} strandedness directories."
-            f"\nGene count directory: {gene_count_dir}\nStrandedness directory: {strand_dir}"
+        _log_and_raise_error(
+            (
+                f"Unequal number of gene count directories and strandedness directories. "
+                f"Found {len(gene_counts_directories)} gene count directories and {len(strandedness_directories)} strandedness directories."
+                f"\nGene count directory: {gene_count_dir}\nStrandedness directory: {strand_dir}"
+            ),
+            error=ValueError,
+            level=LogLevel.ERROR,
         )
 
     # For each study, collect gene count files, fragment files, insert size files, layouts, and strandedness information
@@ -141,9 +200,13 @@ def _organize_gene_counts_files(data_dir: Path) -> list[_StudyMetrics]:
         count_files = list(gene_dir.glob("*.tab"))
         strand_files = list(strand_dir.glob("*.txt"))
         if len(count_files) == 0:
-            raise ValueError(f"No count files found for study '{gene_dir.stem}'.")
+            _log_and_raise_error(f"No count files found for study '{gene_dir.stem}'.", error=ValueError, level=LogLevel.ERROR)
         if len(strand_files) == 0:
-            raise ValueError(f"No strandedness files found for study '{gene_dir.stem}'.")
+            _log_and_raise_error(
+                f"No strandedness files found for study '{gene_dir.stem}'.",
+                error=ValueError,
+                level=LogLevel.ERROR,
+            )
 
         study_metrics.append(
             _StudyMetrics(
@@ -162,9 +225,13 @@ async def _process_first_multirun_sample(strand_file: Path, all_counts_files: li
         strand_information = strand_file.read_text().rstrip("\n").lower()
 
         if strand_information not in ("none", "first_read_transcription_strand", "second_read_transcription_strand"):
-            raise ValueError(
-                f"Unrecognized Strand Information: {strand_information}; "
-                f"expected 'none', 'first_read_transcription_strand', or 'second_read_transcription_strand'"
+            _log_and_raise_error(
+                (
+                    f"Unrecognized Strand Information: {strand_information}; "
+                    f"expected 'none', 'first_read_transcription_strand', or 'second_read_transcription_strand'"
+                ),
+                error=ValueError,
+                level=LogLevel.ERROR,
             )
 
         if strand_information == "none":
@@ -189,9 +256,13 @@ async def _process_standard_replicate(counts_file: Path, strand_file: Path, samp
     strand_information = strand_file.read_text().rstrip("\n").lower()
 
     if strand_information not in ("none", "first_read_transcription_strand", "second_read_transcription_strand"):
-        raise ValueError(
-            f"Unrecognized Strand Information: {strand_information}; "
-            f"expected 'none', 'first_read_transcription_strand', or 'second_read_transcription_strand'"
+        _log_and_raise_error(
+            (
+                f"Unrecognized Strand Information: {strand_information}; "
+                f"expected 'none', 'first_read_transcription_strand', or 'second_read_transcription_strand'"
+            ),
+            error=ValueError,
+            level=LogLevel.ERROR,
         )
 
     if strand_information == "none":
@@ -253,7 +324,7 @@ async def _write_counts_matrix(
     config_df: pd.DataFrame,
     como_context_dir: Path,
     output_counts_matrix_filepath: Path,
-    rna: type_rna,
+    rna: RNAType,
 ) -> pd.DataFrame:
     """Create a counts matrix file by reading gene counts table(s)."""
     study_metrics = _organize_gene_counts_files(data_dir=como_context_dir)
@@ -263,12 +334,13 @@ async def _write_counts_matrix(
     for count in counts:
         final_matrix = count if final_matrix.empty else pd.merge(final_matrix, count, on="ensembl_gene_id", how="outer")
 
-    rna_specific_sample_names = config_df.loc[config_df["library_prep"] == rna, "sample_name"].tolist()
+    rna_specific_sample_names = config_df.loc[config_df["library_prep"] == rna.value, "sample_name"].tolist()
     final_matrix = final_matrix[["ensembl_gene_id", *rna_specific_sample_names]]
 
     output_counts_matrix_filepath.parent.mkdir(parents=True, exist_ok=True)
+    final_matrix.dropna(inplace=True)
     final_matrix.to_csv(output_counts_matrix_filepath, index=False)
-    logger.success(f"Wrote gene count matrix for '{rna}' RNA at '{output_counts_matrix_filepath}'")
+    logger.success(f"Wrote gene count matrix for '{rna.value}' RNA at '{output_counts_matrix_filepath}'")
     return final_matrix
 
 
@@ -287,142 +359,253 @@ async def _create_config_df(  # noqa: C901
     The configuration file created is based on the gene counts matrix.
      If using zFPKM normalization technique, mean fragment lengths will be fetched
     """
-    gene_counts_dir = como_context_dir / gene_count_dirname
-    layout_dir = como_context_dir / layout_dirname
-    strandedness_dir = como_context_dir / strandedness_dirname
-    fragment_sizes_dir = como_context_dir / fragment_sizes_dirname
-    prep_method_dir = como_context_dir / prep_method_dirname
-
-    gene_counts_files = list(gene_counts_dir.rglob("*.tab"))
-    sample_names: list[str] = []
-    fragment_lengths: list[int | float] = []
-    layouts: list[str] = []
-    strands: list[str] = []
-    groups: list[str] = []
-    preparation_method: list[str] = []
-
-    if len(gene_counts_files) == 0:
-        raise FileNotFoundError(f"No gene count files found in '{gene_counts_dir}'.")
-
-    for gene_count_filename in sorted(gene_counts_files):
-        # Match S___R___r___
-        # \d{1,3} matches 1-3 digits
-        # (?:r\d{1,3})? optionally matches a "r" followed by three digits
-        label = re.findall(r"S\d{1,3}R\d{1,3}(?:r\d{1,3})?", gene_count_filename.as_posix())[0]
-        if not label:
-            raise ValueError(
-                f"\n\nFilename of '{gene_count_filename}' is not valid. "
-                f"Should be 'contextName_SXRYrZ.tab', where X is the study/batch number, Y is the replicate number, "
-                f"and Z is the run number."
-                "\n\nIf not a multi-run sample, exclude 'rZ' from the filename."
-            )
+    label_regex: Final = re.compile(r"(?P<study>S\d{1,3})(?P<rep>R\d{1,3})(?:(?P<run>r\d{1,3}))?")
+    gene_counts: list[Path] = list((como_context_dir / gene_count_dirname).rglob("*.tab"))
+    if not gene_counts:
+        _log_and_raise_error(
+            f"No gene count files found in '{gene_count_dirname}'",
+            error=FileNotFoundError,
+            level=LogLevel.ERROR,
+        )
 
-        study_number = re.findall(r"S\d{1,3}", label)[0]
-        rep_number = re.findall(r"R\d{1,3}", label)[0]
-        run_number = re.findall(r"r\d{1,3}", label)
-
-        multi_flag = 0
-        if len(run_number) > 0:
-            if run_number[0] != "r1":
-                continue
-            label_glob = f"{study_number}{rep_number}r*"  # S__R__r*
-            runs = [run for run in gene_counts_files if re.search(label_glob, run.as_posix())]
-            multi_flag = 1
-            frag_files = []
-
-            for run in runs:
-                run_number = re.findall(r"R\d{1,3}", run.as_posix())[0]
-                replicate = re.findall(r"r\d{1,3}", run.as_posix())[0]
-                frag_filename = "".join([context_name, "_", study_number, run_number, replicate, "_fragment_size.txt"])
-                frag_files.append(como_context_dir / fragment_sizes_dirname / study_number / frag_filename)
-
-        layout_files: list[Path] = list(layout_dir.rglob(f"{context_name}_{label}_layout.txt"))
-        strand_files: list[Path] = list(strandedness_dir.rglob(f"{context_name}_{label}_strandedness.txt"))
-        frag_files: list[Path] = list(fragment_sizes_dir.rglob(f"{context_name}_{label}_fragment_size.txt"))
-        prep_files: list[Path] = list(prep_method_dir.rglob(f"{context_name}_{label}_prep_method.txt"))
-
-        layout = "UNKNOWN"
-        if len(layout_files) == 0:
-            logger.warning(
-                f"No layout file found for {label}, writing as 'UNKNOWN', "
-                f"this should be defined if you are using zFPKM or downstream 'rnaseq_gen.py' will not run"
+    auxillary_directories = {
+        "layout": como_context_dir / layout_dirname,
+        "strand": como_context_dir / strandedness_dirname,
+        "fragment": como_context_dir / fragment_sizes_dirname,
+        "prep": como_context_dir / prep_method_dirname,
+    }
+    aux_lookup: dict[str, dict[str, Path]] = {kind: {} for kind in auxillary_directories}
+    for kind, root in auxillary_directories.items():
+        kind: str
+        root: Path
+        for p in root.rglob("*"):
+            if p.is_file():
+                m = label_regex.search(p.stem)
+                if m:
+                    aux_lookup[kind][m.group(0)] = p
+
+    rows: list[SampleConfiguration] = []
+    for gene_count_path in sorted(gene_counts):
+        m = label_regex.search(gene_count_path.as_posix())
+        if not m:
+            _log_and_raise_error(
+                f"Filename '{gene_count_path.name}' does not match contextName_SXRYrZ.tab pattern",
+                error=ValueError,
+                level=LogLevel.ERROR,
             )
-        elif len(layout_files) == 1:
-            with layout_files[0].open("r") as file:
-                layout = file.read().strip()
-        elif len(layout_files) > 1:
-            raise ValueError(f"Multiple matching layout files for {label}, make sure there is only one copy for each replicate in COMO_input")
-
-        strand = "UNKNOWN"
-        if len(strand_files) == 0:
-            logger.warning(
-                f"No strandedness file found for {label}, writing as 'UNKNOWN'. "
-                f"This will not interfere with the analysis since you have already set rnaseq_preprocess.py to "
-                f"infer the strandedness when writing the counts matrix"
+        label = m.group(0)
+        study_number = m["study"]
+        rep_number = m["rep"]
+        sample_id = f"{context_name}_{study_number}{rep_number}"
+
+        layout_path = _require_one([aux_lookup["layout"].get(label)], "layout", label)
+        strand_path = _require_one([aux_lookup["strand"].get(label)], "strand", label)
+        prep_path = _require_one([aux_lookup["prep"].get(label)], "preparation", label)
+
+        layout, strand, prep = await asyncio.gather(
+            *[
+                _read_text(layout_path, default="UNKNOWN"),
+                _read_text(strand_path, default="UNKNOWN"),
+                _read_text(prep_path, default="total", lower=True),
+            ],
+        )
+        if prep not in {"total", "mrna"}:
+            _log_and_raise_error(
+                f"Prep method must be 'total' or 'mrna' (got '{prep}') for {label}",
+                error=ValueError,
+                level=LogLevel.ERROR,
             )
-        elif len(strand_files) == 1:
-            with strand_files[0].open("r") as file:
-                strand = file.read().strip()
-        elif len(strand_files) > 1:
-            raise ValueError(f"Multiple matching strandedness files for {label}, make sure there is only one copy for each replicate in COMO_input")
-
-        prep = "total"
-        if len(prep_files) == 0:
-            logger.warning(f"No prep file found for {label}, assuming 'total', as in 'Total RNA' library preparation")
-        elif len(prep_files) == 1:
-            with prep_files[0].open("r") as file:
-                prep = file.read().strip().lower()
-                if prep not in ["total", "mrna"]:
-                    raise ValueError(f"Prep method must be either 'total' or 'mrna' for {label}")
-        elif len(prep_files) > 1:
-            raise ValueError(f"Multiple matching prep files for {label}, make sure there is only one copy for each replicate in COMO_input")
-
-        mean_fragment_size = 100
-        if len(frag_files) == 0 and prep != RNAPrepMethod.TOTAL.value:
-            logger.warning(
-                f"No fragment file found for {label}, using '100'. You should define this if you are going to use downstream zFPKM normalization"
+
+        fragment_label = f"{context_name}_{label}_fragment_size.txt"
+        frag_paths = [p for p in aux_lookup["fragment"].values() if p.name == fragment_label]
+        if not frag_paths and prep != RNAType.TRNA.value:
+            logger.warning(f"No fragment file for '{label}'; defaulting to 100 bp (needed for zFPKM).")
+            mean_frag = 100.0
+        elif len(frag_paths) == 1 and layout == "single-end":
+            mean_frag = 0.0
+        else:  # 1-N files, paired end
+            dfs: list[pd.DataFrame] = await asyncio.gather(*[_read_file(f, sep="\t", on_bad_lines="skip") for f in frag_paths])
+            for df in dfs:
+                df["meanxcount"] = df["frag_mean"] * df["frag_count"]
+                counts = np.array([df["frag_count"].sum() for df in dfs])
+                means = np.array([(df["meanxcount"] / df["frag_count"].sum()).sum() for df in dfs])
+                mean_frag = float(np.average(means, weights=counts))
+        rows.append(
+            SampleConfiguration(
+                sample_name=sample_id,
+                fragment_length=mean_frag,
+                layout=layout,
+                strand=strand,
+                study=study_number,
+                library_prep=prep,
             )
-        elif len(frag_files) == 1:
-            if layout == "single-end":
-                mean_fragment_size = 0
-            else:
-                if not multi_flag:
-                    frag_df = pd.read_table(frag_files[0], low_memory=False)
-                    frag_df["meanxcount"] = frag_df["frag_mean"] * frag_df["frag_count"]
-                    mean_fragment_size = sum(frag_df["meanxcount"] / sum(frag_df["frag_count"]))
-
-                else:
-                    mean_fragment_sizes = np.array([])
-                    library_sizes = np.array([])
-                    for ff in frag_files:
-                        frag_df = pd.read_table(ff, low_memory=False, sep="\t", on_bad_lines="skip")
-                        frag_df["meanxcount"] = frag_df["frag_mean"] * frag_df["frag_count"]
-                        mean_fragment_size = sum(frag_df["meanxcount"] / sum(frag_df["frag_count"]))
-                        mean_fragment_sizes = np.append(mean_fragment_sizes, mean_fragment_size)
-                        library_sizes = np.append(library_sizes, sum(frag_df["frag_count"]))
-
-                    mean_fragment_size = sum(mean_fragment_sizes * library_sizes) / sum(library_sizes)
-        elif len(frag_files) > 1:
-            raise ValueError(f"Multiple matching fragment files for {label}, make sure there is only one copy for each replicate in COMO_input")
-
-        sample_names.append(f"{context_name}_{study_number}{rep_number}")
-        fragment_lengths.append(mean_fragment_size)
-        layouts.append(layout)
-        strands.append(strand)
-        groups.append(study_number)
-        preparation_method.append(prep)
-
-    out_df = pd.DataFrame(
-        {
-            "sample_name": sample_names,
-            "fragment_length": fragment_lengths,
-            "layout": layouts,
-            "strand": strands,
-            "study": groups,
-            "library_prep": preparation_method,
-        }
-    ).sort_values("sample_name")
-    return out_df
+        )
+
+    df = pd.DataFrame.from_records([asdict(r) for r in rows]).sort_values("sample_name", ignore_index=True)
+    return df
+
+    # 6-3-25: Intentionally left commented-out code to test its replacement
+    # gene_counts_dir = como_context_dir / gene_count_dirname
+    # layout_dir = como_context_dir / layout_dirname
+    # strandedness_dir = como_context_dir / strandedness_dirname
+    # fragment_sizes_dir = como_context_dir / fragment_sizes_dirname
+    # prep_method_dir = como_context_dir / prep_method_dirname
+    #
+    # gene_counts_files = list(gene_counts_dir.rglob("*.tab"))
+    # sample_names: list[str] = []
+    # fragment_lengths: list[int | float] = []
+    # layouts: list[str] = []
+    # strands: list[str] = []
+    # groups: list[str] = []
+    # preparation_method: list[str] = []
+    #
+    # if len(gene_counts_files) == 0:
+    #     _log_and_raise_error(f"No gene count files found in '{gene_counts_dir}'.", error=FileNotFoundError, level=LogLevel.ERROR)
+    #
+    # for gene_count_filename in sorted(gene_counts_files):
+    #     # Match S___R___r___
+    #     # \d{1,3} matches 1-3 digits
+    #     # (?:r\d{1,3})? optionally matches a "r" followed by three digits
+    #     label = re.findall(r"S\d{1,3}R\d{1,3}(?:r\d{1,3})?", gene_count_filename.as_posix())[0]
+    #     if not label:
+    #         _log_and_raise_error(
+    #             (
+    #                 f"\n\nFilename of '{gene_count_filename}' is not valid. "
+    #                 f"Should be 'contextName_SXRYrZ.tab', "
+    #                 f"where X is the study/batch number, Y is the replicate number, "
+    #                 f"and Z is the run number."
+    #                 "\n\nIf not a multi-run sample, exclude 'rZ' from the filename."
+    #             ),
+    #             error=ValueError,
+    #             level=LogLevel.ERROR,
+    #         )
+    #
+    #     study_number = re.findall(r"S\d{1,3}", label)[0]
+    #     rep_number = re.findall(r"R\d{1,3}", label)[0]
+    #     run_number = re.findall(r"r\d{1,3}", label)
+    #
+    #     multi_flag = 0
+    #     if len(run_number) > 0:
+    #         if run_number[0] != "r1":
+    #             continue
+    #         label_glob = f"{study_number}{rep_number}r*"  # S__R__r*
+    #         runs = [run for run in gene_counts_files if re.search(label_glob, run.as_posix())]
+    #         multi_flag = 1
+    #         frag_files = []
+    #
+    #         for run in runs:
+    #             run_number = re.findall(r"R\d{1,3}", run.as_posix())[0]
+    #             replicate = re.findall(r"r\d{1,3}", run.as_posix())[0]
+    #             frag_filename = "".join([context_name, "_", study_number, run_number, replicate, "_fragment_size.txt"])
+    #             frag_files.append(como_context_dir / fragment_sizes_dirname / study_number / frag_filename)
+    #
+    #     layout_files: list[Path] = list(layout_dir.rglob(f"{context_name}_{label}_layout.txt"))
+    #     strand_files: list[Path] = list(strandedness_dir.rglob(f"{context_name}_{label}_strandedness.txt"))
+    #     frag_files: list[Path] = list(fragment_sizes_dir.rglob(f"{context_name}_{label}_fragment_size.txt"))
+    #     prep_files: list[Path] = list(prep_method_dir.rglob(f"{context_name}_{label}_prep_method.txt"))
+    #
+    #     layout = "UNKNOWN"
+    #     if len(layout_files) == 0:
+    #         logger.warning(
+    #             f"No layout file found for {label}, writing as 'UNKNOWN', "
+    #             f"this should be defined if you are using zFPKM or downstream 'rnaseq_gen.py' will not run"
+    #         )
+    #     elif len(layout_files) == 1:
+    #         with layout_files[0].open("r") as file:
+    #             layout = file.read().strip()
+    #     elif len(layout_files) > 1:
+    #         _log_and_raise_error(
+    #             f"Multiple matching layout files for {label}, make sure there is only one copy for each replicate in COMO_input",
+    #             error=ValueError,
+    #             level=LogLevel.ERROR,
+    #         )
+    #
+    #     strand = "UNKNOWN"
+    #     if len(strand_files) == 0:
+    #         logger.warning(
+    #             f"No strandedness file found for {label}, writing as 'UNKNOWN'. "
+    #             f"This will not interfere with the analysis since you have already set rnaseq_preprocess.py to "
+    #             f"infer the strandedness when writing the counts matrix"
+    #         )
+    #     elif len(strand_files) == 1:
+    #         with strand_files[0].open("r") as file:
+    #             strand = file.read().strip()
+    #     elif len(strand_files) > 1:
+    #         _log_and_raise_error(
+    #             f"Multiple matching strandedness files for {label}, make sure there is only one copy for each replicate in COMO_input",
+    #             error=ValueError,
+    #             level=LogLevel.ERROR,
+    #         )
+    #
+    #     prep = "total"
+    #     if len(prep_files) == 0:
+    #         logger.warning(f"No prep file found for {label}, assuming 'total', as in 'Total RNA' library preparation")
+    #     elif len(prep_files) == 1:
+    #         with prep_files[0].open("r") as file:
+    #             prep = file.read().strip().lower()
+    #             if prep not in ["total", "mrna"]:
+    #                 _log_and_raise_error(
+    #                     f"Prep method must be either 'total' or 'mrna' for {label}",
+    #                     error=ValueError,
+    #                     level=LogLevel.ERROR,
+    #                 )
+    #     elif len(prep_files) > 1:
+    #         _log_and_raise_error(
+    #             f"Multiple matching prep files for {label}, make sure there is only one copy for each replicate in COMO_input",
+    #             error=ValueError,
+    #             level=LogLevel.ERROR,
+    #         )
+    #
+    #     mean_fragment_size = 100
+    #     if len(frag_files) == 0 and prep != RNAType.TRNA.value:
+    #         logger.warning(
+    #             f"No fragment file found for {label}, using '100'. You should define this if you are going to use downstream zFPKM normalization"
+    #         )
+    #     elif len(frag_files) == 1:
+    #         if layout == "single-end":
+    #             mean_fragment_size = 0
+    #         else:
+    #             if not multi_flag:
+    #                 frag_df = pd.read_table(frag_files[0], low_memory=False)
+    #                 frag_df["meanxcount"] = frag_df["frag_mean"] * frag_df["frag_count"]
+    #                 mean_fragment_size = sum(frag_df["meanxcount"] / sum(frag_df["frag_count"]))
+    #
+    #             else:
+    #                 mean_fragment_sizes = np.array([])
+    #                 library_sizes = np.array([])
+    #                 for ff in frag_files:
+    #                     frag_df = pd.read_table(ff, low_memory=False, sep="\t", on_bad_lines="skip")
+    #                     frag_df["meanxcount"] = frag_df["frag_mean"] * frag_df["frag_count"]
+    #                     mean_fragment_size = sum(frag_df["meanxcount"] / sum(frag_df["frag_count"]))
+    #                     mean_fragment_sizes = np.append(mean_fragment_sizes, mean_fragment_size)
+    #                     library_sizes = np.append(library_sizes, sum(frag_df["frag_count"]))
+    #
+    #                 mean_fragment_size = sum(mean_fragment_sizes * library_sizes) / sum(library_sizes)
+    #     elif len(frag_files) > 1:
+    #         _log_and_raise_error(
+    #             f"Multiple matching fragment files for {label}, make sure there is only one copy for each replicate in COMO_input",
+    #             error=ValueError,
+    #             level=LogLevel.ERROR,
+    #         )
+    #
+    #     sample_names.append(f"{context_name}_{study_number}{rep_number}")
+    #     fragment_lengths.append(mean_fragment_size)
+    #     layouts.append(layout)
+    #     strands.append(strand)
+    #     groups.append(study_number)
+    #     preparation_method.append(prep)
+    #
+    # out_df = pd.DataFrame(
+    #     {
+    #         "sample_name": sample_names,
+    #         "fragment_length": fragment_lengths,
+    #         "layout": layouts,
+    #         "strand": strands,
+    #         "study": groups,
+    #         "library_prep": preparation_method,
+    #     }
+    # ).sort_values("sample_name")
+    # return out_df
 
 
 async def _create_gene_info_file(
@@ -438,52 +621,60 @@ async def _create_gene_info_file(
     """
 
     async def read_counts(file: Path) -> list[str]:
-        data = await asyncio.to_thread(pd.read_csv if file.suffix == ".csv" else sc.read_h5ad, file)
-        conversion = (
-            await ensembl_to_gene_id_and_symbol(ids=data["ensembl_gene_id"].tolist(), taxon=taxon)
-            if isinstance(data, pd.DataFrame)
-            else await gene_symbol_to_ensembl_and_gene_id(symbols=data.var_names.tolist(), taxon=taxon)
-        )
-        return conversion["entrez_gene_id"].tolist()
+        data = await _read_file(file, h5ad_as_df=False)
 
-    logger.info("Fetching gene info (this may take 1-5 minutes depending on the number of genes and your internet connection)")
+        try:
+            conversion = await (
+                ensembl_to_gene_id_and_symbol(ids=data["ensembl_gene_id"].tolist(), taxon=taxon)
+                if isinstance(data, pd.DataFrame)
+                else gene_symbol_to_ensembl_and_gene_id(symbols=data.var_names.tolist(), taxon=taxon)
+            )
+        except json.JSONDecodeError:
+            _log_and_raise_error(
+                f"Got a JSO decode error for file '{counts_matrix_filepaths}'",
+                error=ValueError,
+                level=LogLevel.CRITICAL,
+            )
+
+        # Remove NA values from entrez_gene_id dataframe column
+        return conversion["entrez_gene_id"].dropna().tolist()
+
+    logger.info("Fetching gene info - this can take up to 5 minutes depending on the number of genes and your internet connection")
     genes = set(chain.from_iterable(await asyncio.gather(*[read_counts(f) for f in counts_matrix_filepaths])))
     gene_data = await MyGene(cache=cache).query(items=list(genes), taxon=taxon, scopes="entrezgene")
     gene_info: pd.DataFrame = pd.DataFrame(
         data=None,
-        columns=pd.Index(data=["ensembl_gene_id", "gene_symbol", "entrez_gene_id", "start_position", "end_position"]),
+        columns=pd.Index(data=["ensembl_gene_id", "gene_symbol", "entrez_gene_id", "size"]),
         index=pd.Index(data=range(len(gene_data))),
     )
     for i, data in enumerate(gene_data):
-        ensembl_ids = data.get("ensembl.gene", "-")
+        ensembl_ids = data.get("genomic_pos.ensemblgene", "-")
         if isinstance(ensembl_ids, list):
             ensembl_ids = ensembl_ids[0]
 
         start_pos = data.get("genomic_pos.start", 0)
-        start_pos = sum(start_pos) / len(start_pos) if isinstance(start_pos, list) else start_pos
+        start_pos: int = int(sum(start_pos) / len(start_pos)) if isinstance(start_pos, list) else int(start_pos)
         end_pos = data.get("genomic_pos.end", 0)
-        end_pos = sum(end_pos) / len(end_pos) if isinstance(end_pos, list) else end_pos
+        end_pos: int = int(sum(end_pos) / len(end_pos)) if isinstance(end_pos, list) else int(end_pos)
 
         gene_info.at[i, "gene_symbol"] = data.get("symbol", "-")
         gene_info.at[i, "entrez_gene_id"] = data.get("entrezgene", "-")
         gene_info.at[i, "ensembl_gene_id"] = ensembl_ids
-        gene_info.at[i, "start_position"] = start_pos
-        gene_info.at[i, "end_position"] = end_pos
+        gene_info.at[i, "size"] = end_pos - start_pos
 
     gene_info = gene_info[((gene_info["entrez_gene_id"] != "-") & (gene_info["ensembl_gene_id"] != "-") & (gene_info["gene_symbol"] != "-"))]
-    gene_info["size"] = gene_info["end_position"].astype(int) - gene_info["start_position"].astype(int)
-    gene_info.drop(columns=["start_position", "end_position"], inplace=True)
     gene_info.sort_values(by="ensembl_gene_id", inplace=True)
+    gene_info.dropna(inplace=True)
     gene_info.to_csv(output_filepath, index=False)
     logger.success(f"Gene Info file written at '{output_filepath}'")
 
 
-async def _create_matrix_file(
+async def _process_como_input(
     context_name: str,
     output_config_filepath: Path,
-    como_context_dir: type_path,
+    como_context_dir: PATH_TYPE,
     output_counts_matrix_filepath: Path,
-    rna: type_rna,
+    rna: RNAType,
 ) -> None:
     config_df = await _create_config_df(context_name, como_context_dir=como_context_dir)
     await _write_counts_matrix(
@@ -493,7 +684,7 @@ async def _create_matrix_file(
         rna=rna,
     )
     with pd.ExcelWriter(output_config_filepath) as writer:
-        subset_config = config_df[config_df["library_prep"] == rna]
+        subset_config = config_df[config_df["library_prep"] == rna.value]
         subset_config.to_excel(writer, sheet_name=context_name, header=True, index=False)
 
 
@@ -509,18 +700,18 @@ async def _process(
     output_mrna_matrix_filepath: Path | None,
     cache: bool,
 ):
-    rna_types: list[tuple[type_rna, Path, Path]] = []
+    rna_types: list[tuple[RNAType, Path, Path]] = []
     if output_trna_config_filepath:
-        rna_types.append(("total", output_trna_config_filepath, output_trna_matrix_filepath))
+        rna_types.append((RNAType.TRNA, output_trna_config_filepath, output_trna_matrix_filepath))
     if output_mrna_config_filepath:
-        rna_types.append(("mrna", output_mrna_config_filepath, output_mrna_matrix_filepath))
+        rna_types.append((RNAType.MRNA, output_mrna_config_filepath, output_mrna_matrix_filepath))
 
     # if provided, iterate through como-input specific directories
     tasks = []
     for rna, output_config_filepath, output_matrix_filepath in rna_types:
         tasks.append(
             asyncio.create_task(
-                _create_matrix_file(
+                _process_como_input(
                     context_name=context_name,
                     output_config_filepath=output_config_filepath,
                     como_context_dir=como_context_dir,
@@ -533,8 +724,15 @@ async def _process(
     await asyncio.gather(*tasks)
 
     # create the gene info filepath based on provided data
+
+    input_files = []
+    if input_matrix_filepath:
+        input_files.extend(input_matrix_filepath)
+    output_trna_matrix_filepath and input_files.append(output_trna_matrix_filepath)
+    output_mrna_matrix_filepath and input_files.append(output_mrna_matrix_filepath)
+
     await _create_gene_info_file(
-        counts_matrix_filepaths=[f for f in [*input_matrix_filepath, output_trna_matrix_filepath, output_mrna_matrix_filepath] if f is not None],
+        counts_matrix_filepaths=input_files,
         output_filepath=output_gene_info_filepath,
         taxon=taxon,
         cache=cache,
@@ -547,12 +745,12 @@ async def rnaseq_preprocess(
     output_gene_info_filepath: Path,
     como_context_dir: Path | None = None,
     input_matrix_filepath: Path | list[Path] | None = None,
-    output_trna_config_filepath: Path | None = None,
-    output_mrna_config_filepath: Path | None = None,
+    output_trna_metadata_filepath: Path | None = None,
+    output_mrna_metadata_filepath: Path | None = None,
     output_trna_count_matrix_filepath: Path | None = None,
     output_mrna_count_matrix_filepath: Path | None = None,
     cache: bool = True,
-    log_level: Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] = "INFO",
+    log_level: LogLevel | str = LogLevel.INFO,
     log_location: str | TextIOWrapper = sys.stderr,
 ) -> None:
     """Preprocesses RNA-seq data for downstream analysis.
@@ -563,8 +761,8 @@ async def rnaseq_preprocess(
     :param context_name: The context/cell type being processed
     :param taxon: The NCBI taxonomy ID
     :param output_gene_info_filepath: Path to the output gene information CSV file
-    :param output_trna_config_filepath: Path to the output tRNA config file (if in "create" mode)
-    :param output_mrna_config_filepath: Path to the output mRNA config file (if in "create" mode)
+    :param output_trna_metadata_filepath: Path to the output tRNA config file (if in "create" mode)
+    :param output_mrna_metadata_filepath: Path to the output mRNA config file (if in "create" mode)
     :param output_trna_count_matrix_filepath: The path to write total RNA count matrices
     :param output_mrna_count_matrix_filepath: The path to write messenger RNA count matrices
     :param como_context_dir: If in "create" mode, the input path(s) to the COMO_input directory of the current context
@@ -574,35 +772,26 @@ async def rnaseq_preprocess(
     :param log_level: The logging level
     :param log_location: The logging location
     """
-    with contextlib.suppress(ValueError):
-        logger.remove(0)
-        logger.add(
-            sink=log_location,
-            level=log_level,
-            format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>",  # noqa: E501
-        )
+    _set_up_logging(level=log_level, location=log_location)
 
     output_gene_info_filepath = output_gene_info_filepath.resolve()
-    como_context_dir = como_context_dir.resolve()
+
+    if como_context_dir:
+        como_context_dir = como_context_dir.resolve()
     input_matrix_filepath = [i.resolve() for i in _listify(input_matrix_filepath)] if input_matrix_filepath else None
-    output_trna_config_filepath = output_trna_config_filepath.resolve() if output_trna_config_filepath else output_trna_config_filepath
-    output_mrna_config_filepath = output_mrna_config_filepath.resolve() if output_mrna_config_filepath else output_mrna_config_filepath
-    output_trna_count_matrix_filepath = (
-        output_trna_count_matrix_filepath.resolve() if output_trna_count_matrix_filepath else output_trna_count_matrix_filepath
-    )
-    output_mrna_count_matrix_filepath = (
-        output_mrna_count_matrix_filepath.resolve() if output_mrna_count_matrix_filepath else output_mrna_count_matrix_filepath
-    )
+    output_trna_metadata_filepath = output_trna_metadata_filepath.resolve() if output_trna_metadata_filepath else None
+    output_mrna_metadata_filepath = output_mrna_metadata_filepath.resolve() if output_mrna_metadata_filepath else None
+    output_trna_count_matrix_filepath = output_trna_count_matrix_filepath.resolve() if output_trna_count_matrix_filepath else None
+    output_mrna_count_matrix_filepath = output_mrna_count_matrix_filepath.resolve() if output_mrna_count_matrix_filepath else None
 
-    input_matrix_filepath = _listify(input_matrix_filepath)
     await _process(
         context_name=context_name,
         taxon=taxon,
         como_context_dir=como_context_dir,
         input_matrix_filepath=input_matrix_filepath,
         output_gene_info_filepath=output_gene_info_filepath,
-        output_trna_config_filepath=output_trna_config_filepath,
-        output_mrna_config_filepath=output_mrna_config_filepath,
+        output_trna_config_filepath=output_trna_metadata_filepath,
+        output_mrna_config_filepath=output_mrna_metadata_filepath,
         output_trna_matrix_filepath=output_trna_count_matrix_filepath,
         output_mrna_matrix_filepath=output_mrna_count_matrix_filepath,
         cache=cache,
diff --git a/main/como/types.py b/main/como/types.py
deleted file mode 100644
index 916d3a25..00000000
--- a/main/como/types.py
+++ /dev/null
@@ -1,75 +0,0 @@
-from __future__ import annotations
-
-from abc import ABC
-from enum import Enum
-from pathlib import Path
-from typing import Literal
-
-from pydantic import BaseModel, ConfigDict
-from pydantic_settings import BaseSettings, SettingsConfigDict
-
-
-class _BaseModel(BaseModel, ABC):
-    model_config = ConfigDict()
-
-
-class _BaseArguments(BaseSettings, ABC):
-    model_config = SettingsConfigDict(
-        cli_parse_args=True,
-        cli_kebab_case=True,
-        nested_model_default_partial_update=True,
-        case_sensitive=True,
-        cli_avoid_json=True,
-        cli_enforce_required=True,
-    )
-
-
-class RNAPrepMethod(Enum):
-    TOTAL = "total"
-    MRNA = "mrna"
-    SCRNA = "scrna"
-
-    @staticmethod
-    def from_string(value: str) -> RNAPrepMethod:
-        """Build a preparation method object from a string."""
-        match_value = "".join(c for c in value if c.isascii()).lower()
-
-        match match_value:
-            case "total" | "trna":
-                return RNAPrepMethod.TOTAL
-            case "mrna":
-                return RNAPrepMethod.MRNA
-            case "scrna":
-                return RNAPrepMethod.SCRNA
-            case _:
-                possible_values = [t.value for t in RNAPrepMethod]
-                raise ValueError(f"Filtering technique must be one of {possible_values}; got: {value}")
-
-
-class FilteringTechnique(Enum):
-    """RNA sequencing filtering capabilities."""
-
-    cpm = "cpm"
-    zfpkm = "zfpkm"
-    tpm = "quantile"
-    umi = "umi"
-
-    @staticmethod
-    def from_string(value: str) -> FilteringTechnique:
-        """Create a filtering technique object from a string."""
-        match value.lower():
-            case "cpm":
-                return FilteringTechnique.cpm
-            case "zfpkm":
-                return FilteringTechnique.zfpkm
-            case "quantile":
-                return FilteringTechnique.tpm
-            case "umi":
-                return FilteringTechnique.umi
-            case _:
-                possible_values = [t.value for t in FilteringTechnique]
-                raise ValueError(f"Got a filtering technique of '{value}'; should be one of: {possible_values}")
-
-
-type_path = str | Path
-type_rna = Literal["total", "mrna"]
diff --git a/main/como/utils.py b/main/como/utils.py
index 7e341d06..fc8c9739 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -1,15 +1,18 @@
 from __future__ import annotations
 
+import asyncio
 import contextlib
+import functools
 import io
 import sys
 from collections.abc import Iterator
-from enum import Enum
+from concurrent.futures import ThreadPoolExecutor
+from io import TextIOWrapper
 from pathlib import Path
-from typing import ClassVar
 
-import aiofiles
+import numpy.typing as npt
 import pandas as pd
+import scanpy as sc
 from fast_bioservices import BioDBNet, Output, Taxon
 from fast_bioservices.pipeline import (
     determine_gene_type,
@@ -19,88 +22,9 @@
 )
 from loguru import logger
 
-__all__ = ["Compartments", "split_gene_expression_data", "stringlist_to_list", "suppress_stdout"]
+from como.data_types import LOG_FORMAT, Algorithm, LogLevel
 
-
-class Algorithm(Enum):
-    GIMME = "GIMME"
-    FASTCORE = "FASTCORE"
-    IMAT = "IMAT"
-    TINIT = "TINIT"
-
-    @staticmethod
-    def from_string(value: str) -> Algorithm:
-        match value.lower():
-            case "gimme":
-                return Algorithm.GIMME
-            case "fastcore":
-                return Algorithm.FASTCORE
-            case "imat":
-                return Algorithm.IMAT
-            case "tinit":
-                return Algorithm.TINIT
-            case _:
-                raise ValueError(f"Unknown solver: {value}")
-
-
-class Compartments:
-    """Convert from compartment "long-hand" to "short-hand".
-
-    Shorthand from: https://cobrapy.readthedocs.io/en/latest/_modules/cobra/medium/annotations.html
-
-    "Extracellular" -> "e"
-    "golgi" -> "g"
-    """
-
-    SHORTHAND: ClassVar[dict[str, list[str]]] = {
-        "ce": ["cell envelope"],
-        "c": [
-            "cytoplasm",
-            "cytosol",
-            "default",
-            "in",
-            "intra cellular",
-            "intracellular",
-            "intracellular region",
-            "intracellular space",
-        ],
-        "er": ["endoplasmic reticulum"],
-        "erm": ["endoplasmic reticulum membrane"],
-        "e": [
-            "extracellular",
-            "extraorganism",
-            "out",
-            "extracellular space",
-            "extra organism",
-            "extra cellular",
-            "extra-organism",
-            "external",
-            "external medium",
-        ],
-        "f": ["flagellum", "bacterial-type flagellum"],
-        "g": ["golgi", "golgi apparatus"],
-        "gm": ["golgi membrane"],
-        "h": ["chloroplast"],
-        "l": ["lysosome"],
-        "im": ["mitochondrial intermembrane space"],
-        "mm": ["mitochondrial membrane"],
-        "m": ["mitochondrion", "mitochondria"],
-        "n": ["nucleus"],
-        "p": ["periplasm", "periplasmic space"],
-        "x": ["peroxisome", "glyoxysome"],
-        "u": ["thylakoid"],
-        "vm": ["vacuolar membrane"],
-        "v": ["vacuole"],
-        "w": ["cell wall"],
-        "s": ["eyespot", "eyespot apparatus", "stigma"],
-    }
-
-    _REVERSE_LOOKUP: ClassVar[dict[str, list[str]]] = {value.lower(): key for key, values in SHORTHAND.items() for value in values}
-
-    @classmethod
-    def get(cls, longhand: str) -> str | None:
-        """Get the short-hand compartment name from the long-hand name."""
-        return cls._REVERSE_LOOKUP.get(longhand.lower(), None)
+__all__ = ["split_gene_expression_data", "stringlist_to_list", "suppress_stdout"]
 
 
 def stringlist_to_list(stringlist: str | list[str]) -> list[str]:
@@ -135,11 +59,16 @@ def stringlist_to_list(stringlist: str | list[str]) -> list[str]:
     return new_list
 
 
-def split_gene_expression_data(expression_data: pd.DataFrame, recon_algorithm: Algorithm | None = None):
+def split_gene_expression_data(
+    expression_data: pd.DataFrame,
+    recon_algorithm: Algorithm | None = None,
+    entrez_as_index: bool = True,
+):
     """Split the gene expression data into single-gene and multiple-gene names.
 
     :param expression_data: The gene expression data to map
     :param recon_algorithm: The recon algorithm used to generate the gene expression data
+    :param entrez_as_index: Should the 'entrez_gene_id' column be set as the index
     :return:
     """
     expression_data.columns = [c.lower() for c in expression_data.columns]
@@ -147,13 +76,15 @@ def split_gene_expression_data(expression_data: pd.DataFrame, recon_algorithm: A
         expression_data.rename(columns={"combine_z": "active"}, inplace=True)
 
     expression_data = expression_data[["entrez_gene_id", "active"]]
-    expression_data.loc[:, "entrez_gene_id"] = expression_data["entrez_gene_id"].astype(str)
-    single_gene_names = expression_data[~expression_data["entrez_gene_id"].str.contains("//")]
-    multiple_gene_names = expression_data[expression_data["entrez_gene_id"].str.contains("//")]
-    split_gene_names = multiple_gene_names.assign(entrez_gene_id=multiple_gene_names["entrez_gene_id"].str.split("///")).explode("entrez_gene_id")
+    single_gene_names = expression_data[~expression_data["entrez_gene_id"].astype(str).str.contains("//")]
+    multiple_gene_names = expression_data[expression_data["entrez_gene_id"].astype(str).str.contains("//")]
+    split_gene_names = multiple_gene_names.assign(entrez_gene_id=multiple_gene_names["entrez_gene_id"].astype(str).str.split("///")).explode(
+        "entrez_gene_id"
+    )
 
     gene_expressions = pd.concat([single_gene_names, split_gene_names], axis=0, ignore_index=True)
-    gene_expressions.set_index("entrez_gene_id", inplace=True)
+    if entrez_as_index:
+        gene_expressions.set_index("entrez_gene_id", inplace=True)
     return gene_expressions
 
 
@@ -183,71 +114,130 @@ async def _format_determination(
     :return: A pandas DataFrame
     """
     requested_output = [requested_output] if isinstance(requested_output, Output) else requested_output
-    cohersion = (await biodbnet.db_find(values=input_values, output_db=requested_output, taxon=taxon)).drop(columns=["Input Type"])
-    cohersion.columns = pd.Index(["input_value", *[o.value.replace(" ", "_").lower() for o in requested_output]])
-    return cohersion
+    coercion = (await biodbnet.db_find(values=input_values, output_db=requested_output, taxon=taxon)).drop(columns=["Input Type"])
+    coercion.columns = pd.Index(["input_value", *[o.value.replace(" ", "_").lower() for o in requested_output]])
+    return coercion
+
 
+async def _read_file(
+    path: Path | io.StringIO | None,
+    h5ad_as_df: bool = True,
+    **kwargs,
+) -> pd.DataFrame | sc.AnnData | None:
+    """Asynchronously read a filepath and return a pandas DataFrame.
 
-async def _async_read_csv(path: Path, **kwargs) -> pd.DataFrame:
-    """Asynchronously reads a CSV file and returns a pandas DataFrame.
+    If the provided path is None, None will also be returned.
+    None may be provided to this function so that `asyncio.gather` can safely be used on all sources
+        (trna, mrna, scrna, proteomics) without needing to check if the user has provided those sources
 
     :param path: The path to read from
-    :param kwargs: Additional arguments to pass to pandas.read_csv
-    :return: A pandas DataFrame
+    :param kwargs: Additional arguments to pass to pandas.read_csv, pandas.read_excel,
+        or scanpy.read_h5ad, depending on the filepath provided
+    :return: None, or a pandas DataFrame or AnnData
     """
-    if not path.exists():
-        raise FileNotFoundError(f"File {path} does not exist")
-    if path.suffix not in {".csv", ".tsv"}:
-        raise ValueError(f"File {path} is not a CSV file")
+    if not path:
+        return None
+
+    if isinstance(path, Path) and not path.exists():
+        _log_and_raise_error(f"File {path} does not exist", error=FileNotFoundError, level=LogLevel.CRITICAL)
+
+    # StringIO is used if a CSV file is read using open() directly
+    if isinstance(path, io.StringIO):
+        return pd.read_csv(path, **kwargs)
+
+    match path.suffix:
+        case ".csv" | ".tsv" | ".txt":
+            if "sep" not in kwargs:
+                kwargs.setdefault("sep", "," if path.suffix == ".csv" else "\t")
+            with path.open("r") as i_stream:
+                content = i_stream.read()
+                return pd.read_csv(io.StringIO(content), **kwargs)
+        case ".xlsx" | ".xls":
+            return pd.read_excel(path, **kwargs)
+        case ".h5ad":
+            adata: sc.AnnData = sc.read_h5ad(path, **kwargs)
+            if h5ad_as_df:
+                df = adata.to_df().T
+                df.index.name = "gene_symbol"
+                df.reset_index(inplace=True)
+                return df
+            return adata
+        case _:
+            _log_and_raise_error(
+                f"Unknown file extension '{path.suffix}'. Valid options are '.tsv', '.csv', '.xlsx', '.xls', or '.h5ad'",
+                error=ValueError,
+                level=LogLevel.CRITICAL,
+            )
+            return None
+
+
+async def get_missing_gene_data(values: list[str] | pd.DataFrame, taxon_id: int | str | Taxon) -> pd.DataFrame:
+    if isinstance(values, list):
+        gene_type = await determine_gene_type(values)
+        if all(v == "gene_symbol" for v in gene_type.values()):
+            return await gene_symbol_to_ensembl_and_gene_id(values, taxon=taxon_id)
+        elif all(v == "ensembl_gene_id" for v in gene_type.values()):
+            return await ensembl_to_gene_id_and_symbol(ids=values, taxon=taxon_id)
+        elif all(v == "entrez_gene_id" for v in gene_type.values()):
+            return await gene_id_to_ensembl_and_gene_symbol(ids=values, taxon=taxon_id)
+        else:
+            logger.critical("Gene data must be of the same type (i.e., all Ensembl, Entrez, or Gene Symbols)")
+            raise ValueError("Gene data must be of the same type (i.e., all Ensembl, Entrez, or Gene Symbols)")
+    else:
+        values: pd.DataFrame  # Re-define type to assist in type hinting
+        if "gene_symbol" in values:
+            return await get_missing_gene_data(values["gene_symbol"].tolist(), taxon_id=taxon_id)
+        elif "entrez_gene_id" in values:
+            return await get_missing_gene_data(values["entrez_gene_id"].tolist(), taxon_id=taxon_id)
+        elif "ensembl_gene_id" in values:
+            return await get_missing_gene_data(values["ensembl_gene_id"].tolist(), taxon_id=taxon_id)
+        else:
+            logger.critical("Unable to find 'gene_symbol', 'entrez_gene_id', or 'ensembl_gene_id' in the input matrix.")
+            raise ValueError("Unable to find 'gene_symbol', 'entrez_gene_id', or 'ensembl_gene_id' in the input matrix.")
+
 
-    kwargs.setdefault("sep", "," if path.suffix == ".csv" else "\t")
-    async with aiofiles.open(path) as f:
-        content = await f.read()
-        return pd.read_csv(io.StringIO(content), **kwargs)
+def _listify(value):
+    """Convert items into a list."""
+    return [value] if not isinstance(value, list) else value
 
 
-async def _async_read_excel(path: Path, **kwargs) -> pd.DataFrame:
-    """Asynchronously reads an Excel file and returns a pandas DataFrame.
+def _num_rows(item: pd.DataFrame | npt.NDArray) -> int:
+    return item.shape[0]
 
-    :param path: The path to read from
-    :param kwargs: Additional arguments to pass to pandas.read_excel
-    :return: A pandas DataFrame
-    """
-    if not path.exists():
-        raise FileNotFoundError(f"File {path} does not exist")
-    if path.suffix not in {".xls", ".xlsx"}:
-        raise ValueError(f"File {path} is not an Excel file")
 
-    async with aiofiles.open(path, "rb") as f:
-        content = await f.read()
-        return pd.read_excel(io.StringIO(content.decode()), **kwargs)
+def _num_columns(item: pd.DataFrame | npt.NDArray) -> int:
+    return item.shape[1]
 
 
-def is_notebook() -> bool:
-    """Check if the current environment is a Jupyter Notebook.
+def return_placeholder_data() -> pd.DataFrame:
+    return pd.DataFrame(data=0, index=pd.Index(data=[0], name="entrez_gene_id"), columns=["expressed", "top"])
 
-    :returns: True if the current environment is a Jupyter Notebook, False otherwise.
-    """
-    try:
-        from IPython import get_ipython
-
-        return get_ipython() is not None
-    except ModuleNotFoundError:
-        return False
-
-
-async def convert_gene_data(values: list[str], taxon_id: int | str | Taxon) -> pd.DataFrame:
-    gene_type = await determine_gene_type(values)
-    if all(v == "gene_symbol" for v in gene_type.values()):
-        return await gene_symbol_to_ensembl_and_gene_id(values, taxon=taxon_id)
-    elif all(v == "ensembl_gene_id" for v in gene_type.values()):
-        return await ensembl_to_gene_id_and_symbol(ids=values, taxon=taxon_id)
-    elif all(v == "entrez_gene_id" for v in gene_type.values()):
-        return await gene_id_to_ensembl_and_gene_symbol(ids=values, taxon=taxon_id)
-    else:
-        raise ValueError("Gene data must be of the same type (i.e., all Ensembl, Entrez, or Gene Symbols)")
 
+def _set_up_logging(
+    level: LogLevel | str,
+    location: str | TextIOWrapper,
+    formatting: str = LOG_FORMAT,
+):
+    if isinstance(level, str):
+        level = LogLevel[level.upper()]
+    with contextlib.suppress(ValueError):
+        logger.remove(0)
+        logger.add(sink=location, level=level.value, format=formatting)
 
-def _listify(value):
-    """Convert items into a list."""
-    return [value] if not isinstance(value, list) else value
+
+def _log_and_raise_error(
+    message: str,
+    *,
+    error: type[BaseException],
+    level: LogLevel,
+) -> None:
+    caller = logger.opt(depth=1)
+    match level:
+        case LogLevel.ERROR:
+            caller.error(message)
+        case LogLevel.CRITICAL:
+            caller.critical(message)
+        case _:
+            raise ValueError(f"When raising an error, LogLevel.ERROR or LogLevel.CRITICAL must be used. Got: {level}")
+
+    raise error(message)
diff --git a/pyproject.toml b/pyproject.toml
index 624ea4d7..f59f4ed3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,24 +19,27 @@ dependencies = [
     "plotly>=5.24.1",
     "pydantic-settings",
     "pydantic>=2.10.3",
+    "seaborn>=0.13.2",
     "scanpy>=1.11.4",
     "scikit-learn>=1.5.2",
     "scipy>=1.7.3",
     "setuptools<60.0",
-    "statsmodels>=0.14",
+    "statsmodels>=0.13.0",
+    "statsmodels>=0.14.0; python_version >= '3.12'",
     "troppo@git+https://github.com/JoshLoecker/troppo@master",
 ]
 
 [project.optional-dependencies]
 gurobi = ["gurobipy>=10.0.0"]
 interactive = [
-    "ipython>=7.0.0",
-    "jupyterlab>=4.0.0",
+    "ipython>=8.0.0",
+    "jupyterlab>=4.3.2"
 ]
 dev = [
+    "hypothesis>=6.122.1",
     "pytest-asyncio>=0.24.0",
     "pytest-cov>=6.0.0",
-    "pytest>=8.0.0",
+    "pytest>=8.3.3",
     "ruff>=0.8.0",
 ]
 
diff --git a/ruff.toml b/ruff.toml
index b65a8693..309140fa 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -1,6 +1,5 @@
 line-length = 150
 extend-include = ["docs/**/*.py", "tests/**/*.py", "**/*.ipynb"]
-exclude = ["__init__.py"]
 
 [format]
 quote-style = "double"
@@ -9,46 +8,48 @@ docstring-code-format = true
 [lint]
 # Linting rules: https://docs.astral.sh/ruff/rules/
 select = [
-    "A", # do not use python builtins for variables or parameters; https://pypi.org/project/flake8-builtins/
-    "ASYNC", # identify asynchronous-related problems; https://pypi.org/project/flake8-async/
-    "B", # find likely bugs and design problems; https://pypi.org/project/flake8-bugbear/
-    "C4", # create better list, set, & dict comprehensions; https://pypi.org/project/flake8-comprehensions/
-    "C90", # check for complexity; https://pypi.org/project/mccabe/
-    "D", # docstring style checker; https://pypi.org/project/pydocstyle/
-    "DOC", # docstring linter; https://pypi.org/project/pydoclint/
-    "E", # style guide checking; https://pypi.org/project/pycodestyle/
-    "F", # check for errors; https://pypi.org/project/pyflakes/
-    "RUF", # ruff-specific linting rules; https://docs.astral.sh/ruff/rules/#ruff-specific-rules-ruf
-    "FA", # use from __future__ import annotations if needed; https://pypi.org/project/flake8-future-annotations/
-    "FURB", # refurbish and modernize Python codebases; https://pypi.org/project/refurb/
-    "I", # sorting imports rules; https://pypi.org/project/isort/
-    "N", # check naming conventions; https://pypi.org/project/pep8-naming/
-    "PERF", # check performance anti-patterns; https://pypi.org/project/perflint/
-    "PT", # check common style issues or inconsistencies in pytest; https://pypi.org/project/flake8-pytest-style/
-    "PTH", # use pathlib where possible; https://pypi.org/project/flake8-use-pathlib/
-    "S", # security testing; https://pypi.org/project/flake8-bandit/
-    "SIM", # check for code that can be simplified; https://pypi.org/project/flake8_simplify/
-    "T20", # do not use prints in production; https://pypi.org/project/flake8-print/
-    "TRY", # prevent Exception handling anti-patterns; https://pypi.org/project/tryceratops/
+    "A",        # do not use python builtins for variables or parameters; https://pypi.org/project/flake8-builtins/
+    "ASYNC",    # identify asynchronous-related problems; https://pypi.org/project/flake8-async/
+    "B",        # find likely bugs and design problems; https://pypi.org/project/flake8-bugbear/
+    "C4",       # create better list, set, & dict comprehensions; https://pypi.org/project/flake8-comprehensions/
+    "C90",      # check for complexity; https://pypi.org/project/mccabe/
+    "D",        # docstring style checker; https://pypi.org/project/pydocstyle/
+    "DOC",      # docstring linter; https://pypi.org/project/pydoclint/
+    "E",        # style guide checking; https://pypi.org/project/pycodestyle/
+    "F",        # check for errors; https://pypi.org/project/pyflakes/
+    "RUF",      # ruff-specific linting rules; https://docs.astral.sh/ruff/rules/#ruff-specific-rules-ruf
+    "FA",       # use from __future__ import annotations if needed; https://pypi.org/project/flake8-future-annotations/
+    "FURB",     # refurbish and modernize Python codebases; https://pypi.org/project/refurb/
+    "I",        # sorting imports rules; https://pypi.org/project/isort/
+    "N",        # check naming conventions; https://pypi.org/project/pep8-naming/
+    "PERF",     # check performance anti-patterns; https://pypi.org/project/perflint/
+    "PT",       # check common style issues or inconsistencies in pytest; https://pypi.org/project/flake8-pytest-style/
+    "PTH",      # use pathlib where possible; https://pypi.org/project/flake8-use-pathlib/
+    "S",        # security testing; https://pypi.org/project/flake8-bandit/
+    "SIM",      # check for code that can be simplified; https://pypi.org/project/flake8_simplify/
+    "T20",      # do not use prints in production; https://pypi.org/project/flake8-print/
+    "TRY",      # prevent Exception handling anti-patterns; https://pypi.org/project/tryceratops/
     "UP"        # upgrade syntax for newer versions; https://pypi.org/project/pyupgrade
 ]
 ignore = [
-    "D100", # allow undocumented public module definitions
-    "D101", # allow undocumented public class
-    "D203", # do not require one blank line before class docstring
-    "D213", # first docstring line should be on the second line
-    "TRY003", # allow exception messages outside the `Exception` class
-    "F401", # allow unused imports
+    "D100",     # allow undocumented public module definitions
+    "D101",     # allow undocumented public class
+    "D104",     # allow undocumented public package
+    "D203",     # do not require one blank line before class docstring
+    "D213",     # first docstring line should be on the second line
+    "TRY003",   # allow exception messages outside the `Exception` class
+    "F401",     # allow unused imports
 ]
 
 [lint.per-file-ignores]
 "tests/*" = [
-    "D101", # allow undocumented public class
-    "D102", # allow undocumented class method
-    "D103", # allow undocumented public method definitions
-    "F811", # allow redefinition of variables, required for pytest fixtures
-    "S101", # allow use of `assert` in test files
+    "D101",     # allow undocumented public class
+    "D102",     # allow undocumented class method
+    "D103",     # allow undocumented public method definitions
+    "D104",     # allow undocumented public package
+    "F811",     # allow redefinition of variables, required for pytest fixtures
+    "S101",     # allow use of `assert` in test files
 ]
 "main/COMO.ipynb" = [
-    "E501", # allow long lines
+    "E501",     # allow long lines
 ]
diff --git a/tests/unit/test_data_types.py b/tests/unit/test_data_types.py
new file mode 100644
index 00000000..45939ba8
--- /dev/null
+++ b/tests/unit/test_data_types.py
@@ -0,0 +1,10 @@
+from como.data_types import SourceTypes
+
+
+def test_source_types():
+    """Validate that source types always go in the order of 'trna', 'mrna', 'scrna', 'protemics'."""
+    expected_order = ["trna", "mrna", "scrna", "proteomics"]
+    for expected, source in zip(expected_order, SourceTypes, strict=True):
+        expected: str
+        source: SourceTypes
+        assert expected == source.value
diff --git a/uv.lock b/uv.lock
index 0dcdbd6e..1a227653 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,4 @@
 version = 1
-revision = 3
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
     "python_full_version >= '3.12'",
@@ -11,156 +10,130 @@ resolution-markers = [
 name = "aiofiles"
 version = "24.1.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247, upload-time = "2024-06-24T11:02:03.584Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5", size = 15896, upload-time = "2024-06-24T11:02:01.529Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5", size = 15896 },
 ]
 
 [[package]]
 name = "aioftp"
-version = "0.27.2"
+version = "0.23.1"
 source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/cc/a3/d1af4b69c09dc9955fdb329de5a9ea73f6a3ba50ac4861882453254ffa99/aioftp-0.27.2.tar.gz", hash = "sha256:7c048c3220081796c59832a6fd9fd8fad97e3cec12a50be3abccf2dcbbeb261a", size = 70248, upload-time = "2025-09-24T21:51:31.297Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/b5/be/8788b5ef25efb86261e4e69b5b21de472c6a5d39d06842dc5c5d19772506/aioftp-0.23.1.tar.gz", hash = "sha256:b80eadd8ca95d2bbbcfabe7de15cbe01ac117b2e7467717374de467bad93570b", size = 62481 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/7c/6c/d56c290696f91c4f0fa4be7cfdb38e710d9735c10c57e481cd4bd6066506/aioftp-0.27.2-py3-none-any.whl", hash = "sha256:83befd4091be0e26eeba44dadebb7e7bea653f7b6bda3ba9b6785d4372a93e10", size = 44292, upload-time = "2025-09-24T21:51:29.448Z" },
+    { url = "https://files.pythonhosted.org/packages/31/69/35104b809db2806da92cda96056efaed4cff8e069c1e8a23ec90787fa3f9/aioftp-0.23.1-py3-none-any.whl", hash = "sha256:62d1e59804afc7e42c57e8b3fa44e03e39630323cc666018a4e870d79dc58ec0", size = 37754 },
 ]
 
 [[package]]
 name = "anndata"
-version = "0.11.4"
+version = "0.10.5.post1"
 source = { registry = "https://pypi.org/simple" }
-resolution-markers = [
-    "python_full_version < '3.11'",
-]
 dependencies = [
-    { name = "array-api-compat", marker = "python_full_version < '3.11'" },
+    { name = "array-api-compat" },
     { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
-    { name = "h5py", marker = "python_full_version < '3.11'" },
-    { name = "natsort", marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "packaging", marker = "python_full_version < '3.11'" },
-    { name = "pandas", marker = "python_full_version < '3.11'" },
-    { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/6e/bb/895fa2e9f8cd6d1c058aa90759da715037d0f11e23713e692537555549d7/anndata-0.11.4.tar.gz", hash = "sha256:4ce08d09d2ccb5f37d32790363bbcc7fc1b79863842296ae4badfaf48c736e24", size = 541143, upload-time = "2025-03-26T11:38:54.566Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/0a/4b/ab615fea52e34579d5c6c7dba86b4f9d7f3cdb6a170b348ec49f34cf4355/anndata-0.11.4-py3-none-any.whl", hash = "sha256:fefebb1480316dfa5a23924aa9f74781d447484421bb0c788b0b2ca5e3b339d2", size = 144472, upload-time = "2025-03-26T11:38:52.547Z" },
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-
-[[package]]
-name = "anndata"
-version = "0.12.2"
-source = { registry = "https://pypi.org/simple" }
-resolution-markers = [
-    "python_full_version >= '3.12'",
-    "python_full_version == '3.11.*'",
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-dependencies = [
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-    { name = "h5py", marker = "python_full_version >= '3.11'" },
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-    { name = "zarr", marker = "python_full_version >= '3.11'" },
+    { name = "h5py" },
+    { name = "natsort" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "pandas" },
+    { name = "scipy" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/b1/42/398550c2099e9017c0dbcead889910f09992367628fee8191446fdea3471/anndata-0.12.2.tar.gz", hash = "sha256:ecb3e0613585f5b464d0d3cfb0043a981b2eb92efaa90aae9042dd3e289c4a0a", size = 586312, upload-time = "2025-08-11T07:20:09.549Z" }
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+    { url = "https://files.pythonhosted.org/packages/42/c7/23c2b5d3dd764650d235b574fdcac8d7252e6ae2f90273f65e95b9ca4e34/anndata-0.10.5.post1-py3-none-any.whl", hash = "sha256:3be9d5863d60d7a72d57a6e0392a3e7f3bb2bbfc79a038ddc53192a41517f5fc", size = 121004 },
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 [[package]]
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 version = "0.7.0"
 source = { registry = "https://pypi.org/simple" }
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 [[package]]
 name = "anyio"
-version = "4.11.0"
+version = "4.6.2.post1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
     { name = "idna" },
     { name = "sniffio" },
-    { name = "typing-extensions" },
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 [[package]]
@@ -370,18 +338,16 @@ dependencies = [
     { name = "boolean-py" },
     { name = "indexed" },
     { name = "matplotlib" },
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy" },
     { name = "optlang" },
     { name = "pandas" },
     { name = "pathos" },
-    { name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "scipy", version = "1.16.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "scipy" },
 ]
 
 [[package]]
 name = "cobra"
-version = "0.29.1"
+version = "0.28.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "appdirs" },
@@ -390,8 +356,7 @@ dependencies = [
     { name = "future" },
     { name = "httpx" },
     { name = "importlib-resources" },
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
-    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy" },
     { name = "optlang" },
     { name = "pandas" },
     { name = "pydantic" },
@@ -400,49 +365,81 @@ dependencies = [
     { name = "ruamel-yaml" },
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     { name = "cobra" },
     { name = "fast-bioservices" },
     { name = "kaleido" },
     { name = "loguru" },
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     { name = "pandas" },
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+    { name = "scipy" },
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@@ -450,6 +447,9 @@ dependencies = [
 
 [package.optional-dependencies]
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+    { name = "commitlint" },
+    { name = "hypothesis" },
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From ae0c52be3696fb3ecff80b11c22b0d315fcd168c Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 6 Oct 2025 13:35:53 -0500
Subject: [PATCH 331/345] Fix RNA-seq Processing and Boolean Activity
 Determination (#227)

Co-authored-by: SarahNakamura <snakamura@unomaha.edu>
---
 .githooks/post-commit                         |   16 +
 .githooks/pre-commit                          |   21 +
 .github/workflows/continuous_integration.yml  |   45 -
 main/COMO.ipynb                               |  303 +-
 main/como/combine_distributions.py            |   13 +-
 main/como/create_context_specific_model.py    |    9 +-
 main/como/data_types.py                       |   60 +-
 main/como/merge_xomics.py                     |  116 +-
 main/como/rnaseq_gen.py                       |  236 +-
 main/como/rnaseq_preprocess.py                |    9 +-
 main/como/sanity_checks/__init__.py           |    0
 main/como/sanity_checks/cobra_sanity_check.py |  366 ++
 main/como/sanity_checks/fast_leak_test.py     |  155 +
 .../sanity_checks/naiveb_behavior_tests.py    |  202 +
 main/como/utils.py                            |  129 +-
 .../boundary_rxns/naiveB_boundary_rxns.csv    |    2 +-
 pyproject.toml                                |    2 +-
 tests/unit/test_data_types.py                 |    2 +-
 uv.lock                                       | 3743 ++++++++++-------
 19 files changed, 3572 insertions(+), 1857 deletions(-)
 create mode 100644 .githooks/post-commit
 create mode 100644 .githooks/pre-commit
 create mode 100644 main/como/sanity_checks/__init__.py
 create mode 100644 main/como/sanity_checks/cobra_sanity_check.py
 create mode 100644 main/como/sanity_checks/fast_leak_test.py
 create mode 100644 main/como/sanity_checks/naiveb_behavior_tests.py

diff --git a/.githooks/post-commit b/.githooks/post-commit
new file mode 100644
index 00000000..8584a67d
--- /dev/null
+++ b/.githooks/post-commit
@@ -0,0 +1,16 @@
+#!/usr/bin/env bash
+
+set -e
+
+# Detect if COMO.ipynb exists in non-pushed git history
+# If it does, clear its cell outputs before committing
+NOTEBOOK="main/COMO.ipynb"
+if git diff-tree --no-commit-id --name-only -r HEAD | grep -q "^$NOTEBOOK$"; then
+  jupyter-nbconvert --clear-output --inplace "$NOTEBOOK"
+
+  # re-commit if the notebook was changed
+  if ! git diff --quiet "$NOTEBOOK"; then
+    git add "$NOTEBOOK"
+    git commit -m "Clear outputs from jupyter notebook"
+  fi
+fi
diff --git a/.githooks/pre-commit b/.githooks/pre-commit
new file mode 100644
index 00000000..814b59f2
--- /dev/null
+++ b/.githooks/pre-commit
@@ -0,0 +1,21 @@
+#!/usr/bin/env bash
+set -e
+
+# Run `ruff check` only on staged files
+files=$(git diff --cached --name-only --diff-filter=ACM | grep '\.py$')
+if [ -z "$files" ]; then
+  exit 0
+fi
+ruff check --no-fix "$files"
+status=$?
+if [ $status -ne 0 ]; then
+  echo "Ruff found issues. Commit aborted."
+  exit 1
+fi
+
+# If `ruff check` is good, then format all staged files
+ruff check --fix --select I "$files"
+ruff format "$files"
+
+
+exit 0
diff --git a/.github/workflows/continuous_integration.yml b/.github/workflows/continuous_integration.yml
index 87acfb20..0db91dea 100644
--- a/.github/workflows/continuous_integration.yml
+++ b/.github/workflows/continuous_integration.yml
@@ -7,51 +7,6 @@ on:
   workflow_dispatch:
 
 jobs:
-  format:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v5
-
-      - name: Strip Notebook Output
-        run: uv tool run --from nbconvert jupyter-nbconvert --clear-output --inplace "main/COMO.ipynb"
-
-      - name: Format Python Imports
-        uses: astral-sh/ruff-action@v3
-        with:
-          args: "check --fix --select I"
-
-      - name: Format code
-        uses: astral-sh/ruff-action@v3
-        with:
-          args: "format"
-
-      - name: Format Notebook
-        uses: astral-sh/ruff-action@v3
-        with:
-          args: "format main/COMO.ipynb"
-
-      - name: Commit Changes
-        uses: stefanzweifel/git-auto-commit-action@v5
-        with:
-          commit_message: "style: format code, Jupyter Notebook(s), and Python imports with `ruff`"
-          file_pattern: "main/como/*.py"
-
-
-  lint:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-
-      - name: Check Lint
-        uses: astral-sh/ruff-action@v3
-        with:
-          args: "check --no-fix --verbose"
-
   test:
     runs-on: ubuntu-latest
     strategy:
diff --git a/main/COMO.ipynb b/main/COMO.ipynb
index 17c1c54b..abfe59ee 100644
--- a/main/COMO.ipynb
+++ b/main/COMO.ipynb
@@ -53,7 +53,7 @@
     "    3. Compare results between perturbed and unperturbed models (i.e., knocked-out models vs non-knocked-out models)\n",
     "    4. Integrate with disease genes and create a score of drug targets"
    ],
-   "id": "7062fd81cf966d2c"
+   "id": "479b5fea813eb4c4"
   },
   {
    "metadata": {},
@@ -223,7 +223,7 @@
     "    </tbody>\n",
     "</table>"
    ],
-   "id": "7dd2ed04bffa67f7"
+   "id": "2ed670131b6f7ce2"
   },
   {
    "metadata": {},
@@ -238,6 +238,13 @@
     "    \"\"\"Get the directory of the COMO.ipynb notebook.\n",
     "\n",
     "    This is used to set the proper input and output paths for the rest of the notebook\n",
+    "\n",
+    "    Returns:\n",
+    "        The directory to the COMO.ipynb notebook\n",
+    "\n",
+    "    Raises:\n",
+    "        FileNotFoundError: If the notebook directory cannot be determined\n",
+    "\n",
     "    \"\"\"\n",
     "    current_dir = Path().cwd()\n",
     "\n",
@@ -249,7 +256,7 @@
     "            raise FileNotFoundError(\"Root directory could not be determined; unable to find 'COMO.ipynb'\")\n",
     "        current_dir = current_dir.parent"
    ],
-   "id": "5c66beeb317e91d2"
+   "id": "1a6e53c173aa9517"
   },
   {
    "metadata": {},
@@ -262,18 +269,29 @@
     "from como.data_types import RNAType\n",
     "from como.rnaseq_preprocess import rnaseq_preprocess\n",
     "\n",
-    "notebook_dir: Path = get_notebook_dir()\n",
     "taxon_id = 9606\n",
     "context_names = [\"naiveB\"]\n",
+    "notebook_dir = get_notebook_dir()\n",
+    "\n",
+    "gene_info_filepath = {context: Path(notebook_dir, \"data\", \"results\", context, \"gene_info.csv\") for context in\n",
+    "                      context_names}\n",
+    "como_context_dir = {context: Path(notebook_dir, \"data\", \"COMO_input\", context) for context in context_names}\n",
+    "trna_matrix_filepath = {context: Path(notebook_dir, \"data\", \"results\", context, \"total-rna\", f\"totalrna_{context}.csv\")\n",
+    "                        for context in context_names}\n",
+    "mrna_matrix_filepath = {context: Path(notebook_dir, \"data\", \"results\", context, \"polya-rna\", f\"polyarna_{context}.csv\")\n",
+    "                        for context in context_names}\n",
+    "proteomics_matrix_filepath = {\n",
+    "    context: Path(notebook_dir, \"data\", \"data_matrices\", context, f\"protein_abundance_{context}.csv\") for context in\n",
+    "    context_names\n",
+    "}\n",
     "\n",
-    "gene_info_filepath = {context: Path(f\"data/results/{context}/gene_info.csv\") for context in context_names}\n",
-    "como_context_dir = {context: Path(f\"data/COMO_input/{context}\") for context in context_names}\n",
-    "trna_matrix_filepath = {context: Path(f\"data/results/{context}/total-rna/totalrna_{context}.csv\") for context in\n",
-    "                        context_names}\n",
-    "mrna_matrix_filepath = {context: Path(f\"data/results/{context}/polya-rna/polyarna_{context}.csv\") for context in\n",
-    "                        context_names}\n",
-    "proteomics_matrix_filepath = {context: Path(f\"data/data_matrices/{context}/protein_abundance_{context}.csv\") for context\n",
-    "                              in context_names}\n",
+    "output_trna_boolean_activity_filepath = {context: Path(notebook_dir, \"data\", \"results\", context,\n",
+    "                                                       f\"totalrna_{context}_boolean_activity.csv\") for context in\n",
+    "                                         context_names}\n",
+    "output_mrna_boolean_activity_filepath = {\n",
+    "    context: Path(notebook_dir, \"data\", \"results\", context, f\"polyarna_{context}_boolean_activity.csv\") for context in\n",
+    "    context_names\n",
+    "}\n",
     "\n",
     "trna_metadata_filepath = Path(\"data/config_sheets/trna_config.xlsx\")\n",
     "mrna_metadata_filepath = Path(\"data/config_sheets/mrna_config.xlsx\")\n",
@@ -283,13 +301,9 @@
     "# If you are using single-cell data, adjust the following lines accordingly\n",
     "scrna_matrix_filepath = {context: Path(f\"data/results/{context}/scrna/scrna_{context}.csv\") for context in\n",
     "                         context_names}\n",
-    "# scrna_matrix_filepath = [Path(f\"data/results/{context}/scrna/scrna_{context}.h5ad\") for context in context_names]\n",
-    "\n",
-    "trna_metadata_filepath = Path(notebook_dir / \"data/config_sheets/trna_config.xlsx\")\n",
-    "mrna_metadata_filepath = Path(notebook_dir / \"data/config_sheets/mrna_config.xlsx\")\n",
-    "proteomics_metadata_filepath = Path(notebook_dir / \"data/config_sheets/proteomics_config.xlsx\")\n"
+    "# scrna_matrix_filepath = [Path(f\"data/results/{context}/scrna/scrna_{context}.h5ad\") for context in context_names]\n"
    ],
-   "id": "e6e217041afb37f6"
+   "id": "9ab22f2fc7fee5d1"
   },
   {
    "metadata": {},
@@ -301,7 +315,7 @@
     "- `taxon_id`: The [NCBI Taxon ID](https://www.ncbi.nlm.nih.gov/taxonomy) to use\n",
     "- `preprocess_mode`: This should be set to `\"create-matrix\"` if you are **not** providing a matrix, otherwise set it to `\"provide-matrix\"`"
    ],
-   "id": "c1755a5725908a92"
+   "id": "25ac28c053dcf5c4"
   },
   {
    "metadata": {},
@@ -326,7 +340,7 @@
     "        log_level=\"INFO\",\n",
     "    )"
    ],
-   "id": "4f0a2906b10b0234"
+   "id": "db198ec6d4d97500"
   },
   {
    "metadata": {},
@@ -359,7 +373,7 @@
     "\n",
     "This method is not recommended, as zFPKM is much more robust for a similar level of \"hands-off\" model building\n"
    ],
-   "id": "9593ba96306b1633"
+   "id": "ce9d89e7703f70c7"
   },
   {
    "metadata": {},
@@ -380,7 +394,7 @@
     "#### Single Cell RNA Sequencing\n",
     "While the Snakemake pipeline does not yet support single-cell alignment, and COMO does not yet support automated configuration file and counts matrix file creation for single-cell alignment output from STAR, it is possible to use single-cell data to create a model with COMO. Because normalization strategies can be applied to single-cell data in the same way it is applied to bulk RNA sequencing, `como/rnaseq_gen.py` can be used with a provided counts matrix and configuration file, from [Step 1](Step-1:-Initialize-and-Preprocess-RNA-seq-data), above. Just like `\"total\"` and `\"mRNA\"`, `como/rnaseq_gen.py` can be executed with `\"SC\"` as the \"`--library-prep`\" argument to help COMO differentiate it from any bulk RNA sequencing data if multiple strategies are being used."
    ],
-   "id": "1a19054351d999ca"
+   "id": "e8aa695a4b89e2db"
   },
   {
    "metadata": {},
@@ -399,13 +413,16 @@
     "- `min_zfpkm`: The cutoff for Counts-Per-Million filtering\n",
     "- `prep_method`: The library method used for preparation. Options are: `\"total\"`, `\"mRNA\"`, or `\"SC\"`,\n"
    ],
-   "id": "b2db83f80dd55833"
+   "id": "68f67ce8ab353051"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-02T19:41:58.018200Z",
+     "start_time": "2025-10-02T19:41:14.603148Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": [
     "from como.rnaseq_gen import FilteringTechnique, rnaseq_gen\n",
     "\n",
@@ -419,12 +436,12 @@
     "for context in context_names:\n",
     "    if context not in trna_matrix_filepath:\n",
     "        continue\n",
-    "    output_zscore_norm_filepath = Path(get_notebook_dir() / f\"data/results/{context}/z_score_normalization.csv\")\n",
+    "    output_zscore_norm_filepath = Path(notebook_dir, \"data\", \"results\", context, \"z_score_normalization.csv\")\n",
     "    await rnaseq_gen(\n",
     "        context_name=context,\n",
     "        input_rnaseq_filepath=trna_matrix_filepath[context],\n",
     "        input_gene_info_filepath=gene_info_filepath[context],\n",
-    "        output_boolean_activity_filepath=trna_matrix_filepath[context],\n",
+    "        output_boolean_activity_filepath=output_trna_boolean_activity_filepath[context],\n",
     "        prep=RNAType.TRNA,\n",
     "        taxon_id=taxon_id,\n",
     "        input_metadata_filepath_or_df=trna_metadata_filepath,\n",
@@ -437,7 +454,21 @@
     "        output_zscore_normalization_filepath=output_zscore_norm_filepath,\n",
     "    )"
    ],
-   "id": "e26de1f2ba0637cd"
+   "id": "2cee097e1a352980",
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'context_names' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
+      "Cell \u001B[0;32mIn[1], line 10\u001B[0m\n\u001B[1;32m      7\u001B[0m technique \u001B[38;5;241m=\u001B[39m FilteringTechnique\u001B[38;5;241m.\u001B[39mZFPKM\n\u001B[1;32m      8\u001B[0m cutoff \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m3\u001B[39m\n\u001B[0;32m---> 10\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m context \u001B[38;5;129;01min\u001B[39;00m \u001B[43mcontext_names\u001B[49m:\n\u001B[1;32m     11\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m context \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m trna_matrix_filepath:\n\u001B[1;32m     12\u001B[0m         \u001B[38;5;28;01mcontinue\u001B[39;00m\n",
+      "\u001B[0;31mNameError\u001B[0m: name 'context_names' is not defined"
+     ]
+    }
+   ],
+   "execution_count": 1
   },
   {
    "metadata": {},
@@ -457,7 +488,7 @@
     "- `min_zfpkm`: The cutoff for Counts-Per-Million filtering\n",
     "- `prep_method`: The library method used for preparation. Options are: `\"total\"`, `\"mRNA\"`, or `\"SC\"`,\n"
    ],
-   "id": "6d11a51afe48123e"
+   "id": "2f2d8d87b47f6d5a"
   },
   {
    "metadata": {},
@@ -479,22 +510,23 @@
     "        continue\n",
     "    await rnaseq_gen(\n",
     "        context_name=context,\n",
+    "        input_metadata_filepath_or_df=mrna_metadata_filepath,\n",
     "        input_rnaseq_filepath=mrna_matrix_filepath[context],\n",
     "        input_gene_info_filepath=gene_info_filepath[context],\n",
-    "        output_boolean_activity_filepath=mrna_matrix_filepath[context],\n",
+    "        output_boolean_activity_filepath=output_mrna_boolean_activity_filepath[context],\n",
     "        prep=RNAType.MRNA,\n",
     "        taxon_id=taxon_id,\n",
-    "        input_metadata_filepath_or_df=mrna_metadata_filepath,\n",
     "        replicate_ratio=replicate_ratio,\n",
     "        high_replicate_ratio=high_confidence_replicate_ratio,\n",
     "        batch_ratio=batch_ratio,\n",
     "        high_batch_ratio=high_confidence_batch_ratio,\n",
     "        technique=technique,\n",
     "        cutoff=cutoff,\n",
-    "        output_zscore_normalization_filepath=None,\n",
+    "        output_zscore_normalization_filepath=Path(get_notebook_dir(),\n",
+    "                                                  f\"data/results/{context}/z_score_normalization.csv\"),\n",
     "    )"
    ],
-   "id": "16b6af66244b601e"
+   "id": "a6d8dd8355ef73a1"
   },
   {
    "metadata": {},
@@ -514,7 +546,7 @@
     "- `min_zfpkm`: The cutoff for Counts-Per-Million filtering\n",
     "- `prep_method`: The library method used for preparation. Options are: `\"total\"`, `\"mRNA\"`, or `\"scrna\"`,\n"
    ],
-   "id": "9bbdfcb6c3f26a73"
+   "id": "567295993b382a06"
   },
   {
    "metadata": {},
@@ -541,17 +573,17 @@
     "        output_boolean_activity_filepath=scrna_matrix_filepath[context],\n",
     "        prep=RNAType.SCRNA,\n",
     "        taxon_id=taxon_id,\n",
-    "        input_metadata_filepath_or_df=Path(\"./data/config_sheets/scrna_config.xlsx\"),\n",
+    "        input_metadata_filepath_or_df=scrna_matrix_filepath,\n",
     "        replicate_ratio=replicate_ratio,\n",
     "        high_replicate_ratio=high_confidence_replicate_ratio,\n",
     "        batch_ratio=batch_ratio,\n",
     "        high_batch_ratio=high_confidence_batch_ratio,\n",
     "        technique=technique,\n",
     "        cutoff=cutoff,\n",
-    "        output_zscore_normalization_filepath=None\n",
+    "        output_zscore_normalization_filepath=None,\n",
     "    )"
    ],
-   "id": "416c98bd32d22a5f"
+   "id": "59a30ef35177f933"
   },
   {
    "metadata": {},
@@ -568,7 +600,7 @@
     "- `high_batch_ratio`: The ratio required before a gene is considered \"high-confidence\" in the study\n",
     "- `quantile`: The cutoff Transcripts-Per-Million quantile for filtering"
    ],
-   "id": "c14f51d9a3eb996c"
+   "id": "6c859f0e5a054c41"
   },
   {
    "metadata": {},
@@ -595,7 +627,7 @@
     "        quantile=25,\n",
     "    )"
    ],
-   "id": "c9e99ca8554c1d6"
+   "id": "eaef4b817dccf696"
   },
   {
    "metadata": {},
@@ -621,7 +653,7 @@
     "- `n_neighbors_context`: N nearest neighbors for context clustering. The default is `\"default\"`, which is the total number of contexts\n",
     "- `seed`: The random seed for clustering algorithm initialization. If not specified, `np.random.randint(0, 100000)` is used"
    ],
-   "id": "8e69203fbb1c1e37"
+   "id": "ddcb4aa5ee3b082f"
   },
   {
    "metadata": {},
@@ -665,7 +697,7 @@
     "\n",
     "!{cmd}"
    ],
-   "id": "d59fdb701aaac1d7"
+   "id": "46758ccca5f32fb1"
   },
   {
    "metadata": {},
@@ -705,7 +737,7 @@
     "\n",
     "Each of the \"weights\" (`total_rna_weight`, `mrna_weight`, etc.) are used to place a significance on each method. Becuase there are many steps in the Dogma from transcription to translation, the gene expression as seen by total RNA or mRNA sequencing may not be representative of the gene's protein expression, and this its metabolic impact. Because of this, you are able to weight each source more (or less) than another."
    ],
-   "id": "85a1f953f0d63449"
+   "id": "32a97d01c869f864"
   },
   {
    "metadata": {},
@@ -729,12 +761,16 @@
     "\n",
     "for context in context_names:\n",
     "    await merge_xomics(\n",
-    "        context_name=context,\n",
     "        taxon_id=taxon_id,\n",
+    "        context_name=context,\n",
+    "        trna_metadata_filepath_or_df=trna_metadata_filepath,\n",
+    "        mrna_metadata_filepath_or_df=mrna_metadata_filepath,\n",
     "        trna_matrix_or_filepath=trna_matrix_filepath[context],\n",
     "        mrna_matrix_or_filepath=mrna_matrix_filepath[context],\n",
     "        scrna_matrix_or_filepath=None,  # scrna_matrix_filepath[context],\n",
     "        proteomic_matrix_or_filepath=None,  # proteomics_matrix_filepath[context],\n",
+    "        trna_boolean_matrix_or_filepath=output_trna_boolean_activity_filepath[context],\n",
+    "        mrna_boolean_matrix_or_filepath=output_mrna_boolean_activity_filepath[context],\n",
     "        trna_weight=total_rna_weight,\n",
     "        mrna_weight=mrna_weight,\n",
     "        scrna_weight=single_cell_weight,\n",
@@ -745,17 +781,23 @@
     "        force_activate_high_confidence=force_activate_high_confidence,\n",
     "        adjust_for_na=adjust_for_na_sources,\n",
     "        merge_zfpkm_distribution=merge_zfpkm_distrubution,\n",
-    "        output_merge_activity_filepath=Path(f\"data/results/{context}/ActiveGenes_{context}_Merged.csv\"),\n",
-    "        output_transcriptomic_details_filepath=Path(f\"data/results/{context}/TranscriptomicDetails_{context}.csv\"),\n",
-    "        output_trna_activity_filepath=Path(f\"data/results/{context}/total-rna/trna_activity_{context}.csv\"),\n",
-    "        output_mrna_activity_filepath=Path(f\"data/results/{context}/mrna/mrna_activity_{context}.csv\"),\n",
-    "        output_scrna_activity_filepath=Path(f\"data/results/{context}/scrna/scrna_activity_{context}.csv\"),\n",
-    "        output_proteomic_activity_filepath=Path(f\"data/results/{context}/proteomics/proteomic_activity_{context}.csv\"),\n",
-    "        output_final_model_scores_filepath=Path(f\"data/results/{context}/model_scores_{context}.csv\"),\n",
-    "        output_figure_dirpath=Path(f\"data/results/{context}/figures\")\n",
+    "        # TODO: Move these paths to be under `notebook_dir`\n",
+    "        # Match variables defined above\n",
+    "        output_merge_activity_filepath=Path(f\"{notebook_dir}/data/results/{context}/ActiveGenes_{context}_Merged.csv\"),\n",
+    "        output_transcriptomic_details_filepath=Path(\n",
+    "            f\"{notebook_dir}/data/results/{context}/TranscriptomicDetails_{context}.csv\"),\n",
+    "        output_trna_activity_filepath=Path(\n",
+    "            f\"{notebook_dir}/data/results/{context}/total-rna/trna_activity_{context}.csv\"),\n",
+    "        output_mrna_activity_filepath=Path(f\"{notebook_dir}/data/results/{context}/mrna/mrna_activity_{context}.csv\"),\n",
+    "        output_scrna_activity_filepath=Path(\n",
+    "            f\"{notebook_dir}/data/results/{context}/scrna/scrna_activity_{context}.csv\"),\n",
+    "        output_proteomic_activity_filepath=Path(\n",
+    "            f\"{notebook_dir}/data/results/{context}/proteomics/proteomic_activity_{context}.csv\"),\n",
+    "        output_final_model_scores_filepath=Path(f\"{notebook_dir}/data/results/{context}/model_scores_{context}.csv\"),\n",
+    "        output_figure_dirpath=Path(f\"{notebook_dir}/data/results/{context}/figures\")\n",
     "    )"
    ],
-   "id": "42b1fdb7d2cdcf0e"
+   "id": "96934d1a17f86090"
   },
   {
    "metadata": {},
@@ -822,7 +864,7 @@
     "- `force_reactions_filename`: The filename of the force reactions to be used. Force reactions will (as the name implies) force the optimizer to use these reactions, **no matter their expression**\n",
     "- `exclude_reactions_filename`: The filename of reactions to exclude from the model, no matter their expression"
    ],
-   "id": "6fb29586aa43edf5"
+   "id": "6294dc0a90e1281"
   },
   {
    "metadata": {},
@@ -830,9 +872,11 @@
    "outputs": [],
    "execution_count": null,
    "source": [
-    "import json\n",
+    "\n",
     "from pathlib import Path\n",
     "\n",
+    "from como.create_context_specific_model import create_context_specific_model\n",
+    "from como.data_types import Algorithm, Solver\n",
     "from como.project import Config\n",
     "\n",
     "# Set your objectives before running!\n",
@@ -848,49 +892,69 @@
     "\n",
     "config = Config()\n",
     "\n",
-    "# Load the output of step 1, which is a dictionary that specifies the merged list of active Gene IDs for each tissue\n",
-    "step1_results_file = config.data_dir / \"results\" \"step1_results_files.json\"\n",
-    "with step1_results_file.open(\"r\") as json_file:\n",
-    "    context_gene_exp = json.load(json_file)\n",
-    "\n",
-    "for recon_algorithm in recon_algorithms:\n",
-    "    for context in context_gene_exp:\n",
-    "        objective = objective_dict[context]\n",
-    "\n",
-    "        if recon_algorithm.upper() in [\"IMAT\", \"TINIT\"]:\n",
-    "            active_genes_filepath = config.data_dir / \"results\" / context / f\"model_scores_{context}.csv\"\n",
-    "        else:\n",
-    "            gene_expression_file = context_gene_exp[context]\n",
-    "            active_genes_filename = Path(gene_expression_file).name\n",
-    "            active_genes_filepath = config.data_dir / \"results\" / context / active_genes_filename\n",
-    "\n",
-    "        general_model_filepath = config.data_dir / \"GeneralModelUpdatedV2.mat\"\n",
-    "        boundary_reactions_filepath = config.data_dir / \"boundary_rxns\" / f\"{context}_boundary_rxns.csv\"\n",
-    "        force_reactions_filepath = config.data_dir / \"force_rxns\" / f\"{context}_force_rxns.csv\"\n",
-    "        exclude_reactions_filepath = config.data_dir / \"exclude_rxns\" / f\"{context}_exclude_rxns.csv\"\n",
-    "\n",
-    "        # fmt: off\n",
-    "        cmd = \" \".join(\n",
-    "            [\n",
-    "                \"python3\", \"como/create_context_specific_model.py\",\n",
-    "                \"--context\", context,\n",
-    "                \"--reference-model-filepath\", general_model_filepath,\n",
-    "                \"--active-genes-filepath\", active_genes_filepath,\n",
-    "                \"--objective\", objective,\n",
-    "                \"--boundary-reactions-filepath\", boundary_reactions_filepath,\n",
-    "                # \"--exclude-reactions-filepath\", exclude_reactions_filepath,\n",
-    "                \"--force-reactions-filepath\", force_reactions_filepath,\n",
-    "                \"--algorithm\", recon_algorithm,\n",
-    "                \"--low-threshold\", str(low_threshold),\n",
-    "                \"--high-threshold\", str(high_threshold),\n",
-    "                \"--solver\", solver,\n",
-    "                \"--output-filetypes\", output_filetypes,\n",
-    "            ]\n",
+    "for recon_alg in recon_algorithms:\n",
+    "    for context in context_names:\n",
+    "        await create_context_specific_model(\n",
+    "            context_name=context,\n",
+    "            reference_model=Path(\n",
+    "                \"/Users/satominakamura/Desktop/Dr.Helikar Lab/COMO/main/data/GeneralModelUpdatedV2.mat\"),\n",
+    "            active_genes_filepath=Path(f\"{notebook_dir}/data/results/{context}/ActiveGenes_{context}_Merged.csv\"),\n",
+    "            output_infeasible_reactions_filepath=Path(\n",
+    "                f\"{notebook_dir}/data/results/{context}/infeasible_reactions_{context}.csv\"),\n",
+    "            output_flux_result_filepath=Path(f\"{notebook_dir}/data/results/{context}/FluxResults_{context}.csv\"),\n",
+    "            output_model_filepaths=Path(\n",
+    "                f\"{notebook_dir}/data/results/{context}/{context}_{recon_alg}_model.json\"),\n",
+    "            objective=\"biomass_maintenance\",\n",
+    "            boundary_rxns_filepath=Path(f\"{notebook_dir}/data/boundary_rxns/{context}_boundary_rxns.csv\"),\n",
+    "            exclude_rxns_filepath=Path(f\"{notebook_dir}/data/exclude_rxns/{context}_exclude_rxns.csv\"),\n",
+    "            force_rxns_filepath=Path(f\"{notebook_dir}/data/force_rxns/{context}_force_rxns.csv\"),\n",
+    "            algorithm=Algorithm.IMAT,\n",
+    "            solver=Solver.GUROBI,\n",
     "        )\n",
-    "        # fmt: on\n",
-    "        !{cmd}"
+    "\n",
+    "# Load the output of step 1, which is a dictionary that specifies the merged list of active Gene IDs for each tissue\n",
+    "# step1_results_file = config.data_dir / \"results\" / \"step1_results_files.json\"\n",
+    "# with step1_results_file.open(\"r\") as json_file:\n",
+    "#     context_gene_exp = json.load(json_file)\n",
+    "#\n",
+    "# for recon_algorithm in recon_algorithms:\n",
+    "#     for context in context_gene_exp:\n",
+    "#         objective = objective_dict[context]\n",
+    "#\n",
+    "#         if recon_algorithm.upper() in [\"IMAT\", \"TINIT\"]:\n",
+    "#             active_genes_filepath = config.data_dir / \"results\" / context / f\"model_scores_{context}.csv\"\n",
+    "#         else:\n",
+    "#             gene_expression_file = context_gene_exp[context]\n",
+    "#             active_genes_filename = Path(gene_expression_file).name\n",
+    "#             active_genes_filepath = config.data_dir / \"results\" / context / active_genes_filename\n",
+    "#\n",
+    "#         general_model_filepath = config.data_dir / \"GeneralModelUpdatedV2.mat\"\n",
+    "#         boundary_reactions_filepath = config.data_dir / \"boundary_rxns\" / f\"{context}_boundary_rxns.csv\"\n",
+    "#         force_reactions_filepath = config.data_dir / \"force_rxns\" / f\"{context}_force_rxns.csv\"\n",
+    "#         exclude_reactions_filepath = config.data_dir / \"exclude_rxns\" / f\"{context}_exclude_rxns.csv\"\n",
+    "#\n",
+    "#         # fmt: off\n",
+    "#         cmd = \" \".join(\n",
+    "#             [\n",
+    "#                 \"python3\", \"como/create_context_specific_model.py\",\n",
+    "#                 \"--context\", context,\n",
+    "#                 \"--reference-model-filepath\", general_model_filepath,\n",
+    "#                 \"--active-genes-filepath\", active_genes_filepath,\n",
+    "#                 \"--objective\", objective,\n",
+    "#                 \"--boundary-reactions-filepath\", boundary_reactions_filepath,\n",
+    "#                 # \"--exclude-reactions-filepath\", exclude_reactions_filepath,\n",
+    "#                 \"--force-reactions-filepath\", force_reactions_filepath,\n",
+    "#                 \"--algorithm\", recon_algorithm,\n",
+    "#                 \"--low-threshold\", str(low_threshold),\n",
+    "#                 \"--high-threshold\", str(high_threshold),\n",
+    "#                 \"--solver\", solver,\n",
+    "#                 \"--output-filetypes\", output_filetypes,\n",
+    "#             ]\n",
+    "#         )\n",
+    "#         # fmt: on\n",
+    "#         !{cmd}"
    ],
-   "id": "b24c246aefd4c71"
+   "id": "f9b3b57f5e0e177e"
   },
   {
    "metadata": {},
@@ -915,7 +979,7 @@
     "- `exampleTissue`: This is the name of the tissue context\n",
     "- `ALGORITHM`: This is the algorithm (`recon_algorithm`) used in the above model creation step\n"
    ],
-   "id": "efe6949ce8148d42"
+   "id": "41a3d196ee25c54a"
   },
   {
    "metadata": {},
@@ -930,6 +994,7 @@
     "\n",
     "config = Config()\n",
     "\n",
+    "recon_algorithms = [\"IMAT\"]\n",
     "user_map_dir = config.data_dir / \"local_files\" / \"maps\"\n",
     "map_dict = {\n",
     "    \"trypto\": f\"{config.data_dir}/maps/RECON1/RECON1.tryptophan_metabolism.json\",\n",
@@ -952,9 +1017,9 @@
     "    if file not in map_dict.values():\n",
     "        map_dict[file.stem] = file\n",
     "\n",
-    "for recon_algorithm in recon_algorithms:\n",
-    "    for context in context_gene_exp:\n",
-    "        model_json = config.data_dir / \"results\" / context / f\"{context}_SpecificModel_{recon_algorithm}.json\"\n",
+    "for recon_alg in recon_algorithms:\n",
+    "    for context in context_names:\n",
+    "        model_json = Path(config.data_dir, \"results\", context, f\"{context}_SpecificModel_{recon_alg}.json\")\n",
     "\n",
     "        logger.info(f\"Loading '{context}', this may take some time...\")\n",
     "        model = cobra.io.load_json_model(model_json)\n",
@@ -974,14 +1039,14 @@
     "            builder.reaction_no_data_color = \"#8e8e8e\"\n",
     "\n",
     "            builder.save_html(\n",
-    "                config.data_dir / \"results\" / context / \"figures\" / f\"{key}_map_{context}_{recon_algorithm}.html\"\n",
+    "                Path(config.data_dir, \"results\", context, \"figures\", f\"{key}_map_{context}_{recon_alg}.html\")\n",
     "            )\n",
     "\n",
-    "        out_dir = config.data_dir / \"results\" / context\n",
-    "        report_file = out_dir / f\"memote_report_{context}_{recon_algorithm}.html\"\n",
-    "        model_file = out_dir / f\"{context}_SpecificModel_{recon_algorithm}.xml\"\n",
-    "        log_dir = out_dir / \"memote\"\n",
-    "        log_file = log_dir / f\"{context}_{recon_algorithm}_memote.log\"\n",
+    "        out_dir = Path(config.data_dir, \"results\", context)\n",
+    "        report_file = Path(out_dir, f\"memote_report_{context}_{recon_alg}.html\")\n",
+    "        model_file = Path(out_dir, f\"{context}_SpecificModel_{recon_alg}.xml\")\n",
+    "        log_dir = Path(out_dir, \"memote\")\n",
+    "        log_file = Path(log_dir, f\"{context}_{recon_alg}_memote.log\")\n",
     "\n",
     "        if not log_dir.exists():\n",
     "            log_dir.mkdir(parents=True, exist_ok=True)\n",
@@ -992,7 +1057,7 @@
     "\n",
     "        !{cmd}"
    ],
-   "id": "5a31663187113c5d"
+   "id": "b26399623722713c"
   },
   {
    "metadata": {},
@@ -1011,7 +1076,7 @@
     "- `data_source`: The datasource you are using for disease analysis. This should be`\"rnaseq\"`\n",
     "- `taxon_id`: The [NCBI Taxon ID](https://www.ncbi.nlm.nih.gov/taxonomy) to use for disease analysis"
    ],
-   "id": "14019b16b37a1aa8"
+   "id": "1a6ec8a4642fef1c"
   },
   {
    "metadata": {},
@@ -1023,7 +1088,7 @@
     "\n",
     "disease_names = [\"arthritis\", \"lupus_a\", \"lupus_b\"]\n",
     "data_source = \"rnaseq\"\n",
-    "taxon_id = \"human\"\n",
+    "taxon_id = 9606\n",
     "\n",
     "for context_name in stringlist_to_list(context_names):\n",
     "    disease_config_file = f\"disease_data_inputs_{context_name}.xlsx\"\n",
@@ -1042,7 +1107,7 @@
     "\n",
     "    !{cmd}"
    ],
-   "id": "b31e5ca8a1bd8ce5"
+   "id": "2b888b38e463c9d5"
   },
   {
    "metadata": {},
@@ -1077,7 +1142,7 @@
     "\n",
     "- `solver`: The solver you would like to use. Available options are `\"gurobi\"` or `\"glpk\"`\n"
    ],
-   "id": "320beb32340a1b9e"
+   "id": "7c9a97233ba63a4a"
   },
   {
    "metadata": {},
@@ -1102,7 +1167,7 @@
     "\n",
     "drug_raw_file = \"Repurposing_Hub_export.txt\"\n",
     "for context in stringlist_to_list(context_names):\n",
-    "    for recon_algorithm in recon_algorithms:\n",
+    "    for recon_alg in recon_algorithms:\n",
     "        for disease in disease_names:\n",
     "            disease_path = config.data_dir / \"results\" / context / disease\n",
     "            out_dir = config.data_dir / \"results\" / context / disease\n",
@@ -1125,7 +1190,7 @@
     "                tissue_specific_model_filepath = model_files[context]\n",
     "            else:\n",
     "                tissue_specific_model_filepath = (\n",
-    "                        config.data_dir / \"results\" / context / f\"{context}_SpecificModel_{recon_algorithm}.mat\"\n",
+    "                        config.data_dir / \"results\" / context / f\"{context}_SpecificModel_{recon_alg}.mat\"\n",
     "                )\n",
     "\n",
     "            # fmt: off\n",
@@ -1142,16 +1207,22 @@
     "            ]\n",
     "            # fmt: on\n",
     "\n",
-    "            if recon_algorithm == \"IMAT\":\n",
+    "            if recon_alg == \"IMAT\":\n",
     "                cmd.extend([\"--reference-flux-file\", config.data_dir / \"results\" / context / \"IMAT_flux.csv\"])\n",
     "\n",
     "            cmd = \" \".join(cmd)\n",
     "            !{cmd}"
    ],
-   "id": "46d37e244a62c039"
+   "id": "be644a9283c83408"
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "name": "python3",
+   "language": "python",
+   "display_name": "Python 3 (ipykernel)"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }
diff --git a/main/como/combine_distributions.py b/main/como/combine_distributions.py
index 2d2063df..535686ad 100644
--- a/main/como/combine_distributions.py
+++ b/main/como/combine_distributions.py
@@ -17,6 +17,7 @@
     _OutputCombinedSourceFilepath,
     _SourceWeights,
 )
+from como.graph import z_score_distribution as graph_zscore_distribution
 from como.utils import (
     _num_columns,
 )
@@ -143,10 +144,14 @@ def _combine_z_distribution_for_context(
         return pd.DataFrame({"ensembl_gene_id": [], "combine_z": []})
 
     z_matrices = [
-        res.z_score_matrix.set_index("ensembl_gene_id").rename(columns=dict.fromkeys([res.type.value for res in zscore_results]))
-        for res in zscore_results
+        result.z_score_matrix.set_index("ensembl_gene_id").rename(columns=dict.fromkeys(result.z_score_matrix.columns[1:], result.type.value))
+        for result in zscore_results
     ]
-    z_matrix = pd.concat(z_matrices, axis=1, join="outer").reset_index()
+    z_matrix = pd.DataFrame()
+    for matrix in z_matrices:
+        z_matrix = z_matrix.merge(right=matrix, left_index=True, right_index=True, how="outer") if not z_matrix.empty else matrix
+    z_matrix = z_matrix.reset_index(drop=False)
+    # z_matrix = pd.concat(z_matrices, axis=1, join="outer").reset_index()
     if _num_columns(z_matrix) <= 1:
         logger.trace(f"Only 1 source exists for '{context}', returning dataframe as-is becuase no data exists to combine")
         z_matrix.columns = ["ensembl_gene_id", "combine_z"]
@@ -256,7 +261,7 @@ async def _begin_combining_distributions(
                 weight=source_weights[source.value],
             )
         )
-        merged_source_results.to_csv(output_filepaths[source.value])
+        merged_source_results.to_csv(output_filepaths[source.value], index=False)
         logger.success(f"Wrote z-scores for source '{source.value}' in context '{context_name}' to '{output_filepaths[source.value]}'")
 
     logger.trace(f"Combining z-score distributions for all sources in context '{context_name}'")
diff --git a/main/como/create_context_specific_model.py b/main/como/create_context_specific_model.py
index 90226076..6f004254 100644
--- a/main/como/create_context_specific_model.py
+++ b/main/como/create_context_specific_model.py
@@ -199,14 +199,14 @@ def _build_with_imat(
     upper_bounds: Sequence[float],
     expr_vector: npt.NDArray,
     expr_thresh: tuple[float, float],
-    force_gene_ids: Sequence[int],
+    force_reaction_indices: Sequence[int],
     solver: str,
-) -> (cobra.Model, pd.DataFrame):
+) -> tuple[cobra.Model, pd.DataFrame]:
     expr_vector = np.array(expr_vector)
     properties = IMATProperties(
         exp_vector=expr_vector,
         exp_thresholds=expr_thresh,
-        core=force_gene_ids,
+        core=force_reaction_indices,
         epsilon=0.01,
         solver=solver.upper(),
     )
@@ -346,6 +346,7 @@ async def _build_model(  # noqa: C901
     if objective not in force_reactions:
         force_reactions.append(objective)
     reference_model = _set_boundaries(reference_model, boundary_reactions, lower_bounds, upper_bounds)
+    reference_model.objective = objective
     reference_model.solver = solver.lower()
 
     # check number of unsolvable reactions for reference model under media assumptions
@@ -426,7 +427,7 @@ async def _build_model(  # noqa: C901
                 upper_bounds=upper_bounds,
                 expr_vector=expression_vector,
                 expr_thresh=expression_threshold,
-                force_gene_ids=force_reaction_indices,
+                force_reaction_indices=force_reaction_indices,
                 solver=solver,
             )
             imat_reactions = flux_df.rxn
diff --git a/main/como/data_types.py b/main/como/data_types.py
index 420471a6..977a5a20 100644
--- a/main/como/data_types.py
+++ b/main/como/data_types.py
@@ -14,38 +14,38 @@
 LOG_FORMAT = "<green>{time:YYYY-MM-DD HH:mm:ss}</> | <level>{level:<8}</> | <cyan>{name}</>:<cyan>{line}</> - <level>{message}</>"
 
 
-class AdjustmentMethod(Enum):
+class AdjustmentMethod(str, Enum):
     """Adjustment method for expression requirement based on differences in number of provided data source types."""
 
-    PROGRESSIVE = "progressive"
-    REGRESSIVE = "regressive"
-    FLAT = "flat"
-    CUSTOM = "custom"
+    PROGRESSIVE = "PROGRESSIVE"
+    REGRESSIVE = "REGRESSIVE"
+    FLAT = "FLAT"
+    CUSTOM = "CUSTOM"
 
 
-class Algorithm(Enum):
+class Algorithm(str, Enum):
     GIMME = "GIMME"
     FASTCORE = "FASTCORE"
     IMAT = "IMAT"
     TINIT = "TINIT"
 
 
-class FilteringTechnique(Enum):
+class FilteringTechnique(str, Enum):
     """RNA sequencing filtering capabilities."""
 
-    CPM = "cpm"
-    ZFPKM = "zfpkm"
-    TPM = "tpm"
-    UMI = "umi"
+    CPM = "CPM"
+    ZFPKM = "ZFPKM"
+    TPM = "TPM"
+    UMI = "UMI"
 
 
-class GeneIdentifier(Enum):
-    ENSEMBL_GENE_ID = "ensembl_gene_id"
-    ENTREZ_GENE_ID = "entrez_gene_id"
-    GENE_SYMBOL = "gene_symbol"
+class GeneIdentifier(str, Enum):
+    ENSEMBL_GENE_ID = "ENSEMBL_GENE_ID"
+    ENTREZ_GENE_ID = "ENTREZ_GENE_ID"
+    GENE_SYMBOL = "GENE_SYMBOL"
 
 
-class LogLevel(Enum):
+class LogLevel(int, Enum):
     TRACE = 5
     DEBUG = 10
     INFO = 20
@@ -56,13 +56,13 @@ class LogLevel(Enum):
     NONE = 100
 
 
-class RNAType(Enum):
-    TRNA = "total"
-    MRNA = "mrna"
-    SCRNA = "scrna"
+class RNAType(str, Enum):
+    TRNA = "TOTAL"
+    MRNA = "MRNA"
+    SCRNA = "SCRNA"
 
 
-class Solver(Enum):
+class Solver(str, Enum):
     """Solver used to seed context specific model."""
 
     GLPK = "GLPK"
@@ -71,11 +71,11 @@ class Solver(Enum):
     GLPK_EXACT = "GLPK_EXACT"
 
 
-class SourceTypes(Enum):
-    TRNA = "trna"
-    MRNA = "mrna"
-    SCRNA = "scrna"
-    PROTEOMICS = "proteomics"
+class SourceTypes(str, Enum):
+    TRNA = "TRNA"
+    MRNA = "MRNA"
+    SCRNA = "SCRNA"
+    PROTEOMICS = "PROTEOMICS"
 
 
 class PeakIdentificationParameters(NamedTuple):
@@ -227,10 +227,10 @@ def __iter__(self) -> Iterator[tuple[SourceTypes, pd.DataFrame | None]]:
 
 @dataclass
 class _BatchNames(_BaseDataType):
-    trna: list[_BatchEntry]
-    mrna: list[_BatchEntry]
-    scrna: list[_BatchEntry]
-    proteomics: list[_BatchEntry]
+    trna: list[_BatchEntry] = field(default_factory=list)
+    mrna: list[_BatchEntry] = field(default_factory=list)
+    scrna: list[_BatchEntry] = field(default_factory=list)
+    proteomics: list[_BatchEntry] = field(default_factory=list)
 
 
 @dataclass
diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index fda5d9e4..c017bf4a 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -2,8 +2,8 @@
 
 import asyncio
 import sys
-from io import TextIOWrapper
 from pathlib import Path
+from typing import TextIO, cast
 
 import numpy as np
 import pandas as pd
@@ -62,7 +62,7 @@ def load_dummy_dict():
     if not filename or filename == "None":  # not using this data type, use empty dummy data matrix
         return load_dummy_dict()
 
-    inquiry_full_path = config.data_dir / "config_sheets" / filename
+    inquiry_full_path = Path(config.data_dir, "config_sheets", filename)
     if not inquiry_full_path.exists():
         _log_and_raise_error(
             f"Config file not found at {inquiry_full_path}",
@@ -70,20 +70,7 @@ def load_dummy_dict():
             level=LogLevel.ERROR,
         )
 
-    match prep_method:
-        case RNAType.TRNA:
-            filename = f"{RNAType.TRNA.value}_{context_name}.csv"
-        case RNAType.MRNA:
-            filename = f"{RNAType.MRNA.value}_{context_name}.csv"
-        case RNAType.SCRNA:
-            filename = f"{RNAType.SCRNA.value}_{context_name}.csv"
-        case _:
-            _log_and_raise_error(
-                f"Unsupported RNA-seq library type: {prep_method.value}. Must be one of {', '.join(RNAType)}.",
-                error=ValueError,
-                level=LogLevel.ERROR,
-            )
-
+    filename: str = f"{prep_method.value}_{context_name}.csv"
     save_filepath = config.result_dir / context_name / prep_method.value / filename
     if save_filepath.exists():
         logger.debug(f"Loading RNA-seq data from: {save_filepath}")
@@ -238,7 +225,7 @@ async def _get_transcriptmoic_details(merged_df: pd.DataFrame, taxon_id: int) ->
     logger.trace(f"Querying MyGene for details on {len(transcriptomic_df)} genes")
     for i, detail in enumerate(
         await my_gene.query(
-            items=transcriptomic_df["entrez_gene_id"].tolist(),
+            items=transcriptomic_df["entrez_gene_id"].astype(int).tolist(),
             taxon=taxon_id,
             scopes="entrezgene",
         )
@@ -485,8 +472,8 @@ def _build_batches(
     scrna_metadata: pd.DataFrame | None,
     proteomic_metadata: pd.DataFrame | None,
 ) -> _BatchNames:
-    batch_names = _BatchNames(**{source.name.lower(): [] for source in SourceTypes})
-    for source, metadata in zip(SourceTypes, [trna_metadata, mrna_metadata, scrna_metadata, proteomic_metadata], strict=True):
+    batch_names = _BatchNames()
+    for source, metadata in zip(SourceTypes.__members__.values(), [trna_metadata, mrna_metadata, scrna_metadata, proteomic_metadata], strict=True):
         source: SourceTypes
         metadata: pd.DataFrame
         if metadata is None:
@@ -560,7 +547,7 @@ async def merge_xomics(  # noqa: C901
     weighted_z_floor: int = -6,
     weighted_z_ceiling: int = 6,
     log_level: LogLevel = LogLevel.INFO,
-    log_location: str | TextIOWrapper = sys.stderr,
+    log_location: str | TextIO = sys.stderr,
 ):
     """Merge expression tables of multiple sources (RNA-seq, proteomics) into one."""
     _set_up_logging(level=log_level, location=log_location)
@@ -588,26 +575,20 @@ async def merge_xomics(  # noqa: C901
     ):
         _log_and_raise_error("No data was passed!", error=ValueError, level=LogLevel.ERROR)
 
-    if adjust_method not in AdjustmentMethod:
-        _log_and_raise_error(
-            f"Adjustment method must be one of {AdjustmentMethod}; got: {adjust_method}",
-            error=ValueError,
-            level=LogLevel.ERROR,
-        )
-
-    if expression_requirement < 1:
+    if expression_requirement and expression_requirement < 1:
         logger.warning(f"Expression requirement must be at least 1! Setting to the minimum of 1 now. Got: {expression_requirement}")
         expression_requirement = 1
 
     if expression_requirement is None:
-        expression_requirement = sum(
-            test is not None
-            for test in (
+        expression_requirement = sum(  # Get sum of non-None source inputs
+            1
+            for i in (
                 trna_matrix_or_filepath,
                 mrna_matrix_or_filepath,
                 scrna_matrix_or_filepath,
                 proteomic_matrix_or_filepath,
             )
+            if i
         )
         logger.debug(f"Expression requirement not specified; setting to {expression_requirement}")
 
@@ -628,51 +609,52 @@ async def merge_xomics(  # noqa: C901
         output_figure_dirpath.mkdir(parents=True, exist_ok=True)
 
     # Build trna items
-    trna_matrix: pd.DataFrame | None
-    trna_boolean_matrix: pd.DataFrame | None
-    trna_metadata: pd.DataFrame | None
-    trna_matrix, trna_boolean_matrix, trna_metadata = await asyncio.gather(
-        *[
-            _read_file(trna_matrix_or_filepath),
-            _read_file(trna_boolean_matrix_or_filepath),
-            _read_file(trna_metadata_filepath_or_df),
-        ]
+    # `cast` helps type checkers know what types we are dealing with - costs no runtime performance
+    (trna_matrix, trna_boolean_matrix, trna_metadata) = cast(
+        typ=tuple[pd.DataFrame | None, pd.DataFrame | None, pd.DataFrame | None],
+        val=await asyncio.gather(
+            *[
+                _read_file(trna_matrix_or_filepath, h5ad_as_df=True),
+                _read_file(trna_boolean_matrix_or_filepath, h5ad_as_df=True),
+                _read_file(trna_metadata_filepath_or_df, h5ad_as_df=True),
+            ]
+        ),
     )
 
     # Build mrna items
-    mrna_matrix: pd.DataFrame | None
-    mrna_boolean_matrix: pd.DataFrame | None
-    mrna_metadata: pd.DataFrame | None
-    mrna_matrix, mrna_boolean_matrix, mrna_metadata = await asyncio.gather(
-        *[
-            _read_file(mrna_matrix_or_filepath),
-            _read_file(mrna_boolean_matrix_or_filepath),
-            _read_file(mrna_metadata_filepath_or_df),
-        ]
+    (mrna_matrix, mrna_boolean_matrix, mrna_metadata) = cast(
+        typ=tuple[pd.DataFrame | None, pd.DataFrame | None, pd.DataFrame | None],
+        val=await asyncio.gather(
+            *[
+                _read_file(mrna_matrix_or_filepath),
+                _read_file(mrna_boolean_matrix_or_filepath),
+                _read_file(mrna_metadata_filepath_or_df),
+            ]
+        ),
     )
 
     # build scrna items
-    scrna_matrix: pd.DataFrame | None
-    scrna_boolean_matrix: pd.DataFrame | None
-    scrna_metadata: pd.DataFrame | None
-    scrna_matrix, scrna_boolean_matrix, scrna_metadata = await asyncio.gather(
-        *[
-            _read_file(scrna_matrix_or_filepath),
-            _read_file(scrna_boolean_matrix_or_filepath),
-            _read_file(scrna_metadata_filepath_or_df),
-        ]
+    (scrna_matrix, scrna_boolean_matrix, scrna_metadata) = cast(
+        typ=tuple[pd.DataFrame | None, pd.DataFrame | None, pd.DataFrame | None],
+        val=await asyncio.gather(
+            *[
+                _read_file(scrna_matrix_or_filepath),
+                _read_file(scrna_boolean_matrix_or_filepath),
+                _read_file(scrna_metadata_filepath_or_df),
+            ]
+        ),
     )
 
     # build proteomic items
-    proteomic_matrix: pd.DataFrame | None
-    proteomic_boolean_matrix: pd.DataFrame | None
-    proteomic_metadata: pd.DataFrame | None
-    proteomic_matrix, proteomic_boolean_matrix, proteomic_metadata = await asyncio.gather(
-        *[
-            _read_file(proteomic_matrix_or_filepath),
-            _read_file(proteomic_boolean_matrix_or_filepath),
-            _read_file(proteomic_metadata_filepath_or_df),
-        ]
+    (proteomic_matrix, proteomic_boolean_matrix, proteomic_metadata) = cast(
+        typ=tuple[pd.DataFrame | None, pd.DataFrame | None, pd.DataFrame | None],
+        val=await asyncio.gather(
+            *[
+                _read_file(proteomic_matrix_or_filepath),
+                _read_file(proteomic_boolean_matrix_or_filepath),
+                _read_file(proteomic_metadata_filepath_or_df),
+            ]
+        ),
     )
 
     source_weights = _SourceWeights(trna=trna_weight, mrna=mrna_weight, scrna=scrna_weight, proteomics=proteomic_weight)
diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index fbf9f441..058a6dc8 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -8,17 +8,15 @@
 from concurrent.futures import Future, ProcessPoolExecutor, as_completed
 from dataclasses import dataclass, field
 from enum import Enum
-from io import TextIOWrapper
 from pathlib import Path
-from typing import NamedTuple
+from typing import NamedTuple, TextIO
 
 import matplotlib.pyplot as plt
 import numpy as np
 import numpy.typing as npt
 import pandas as pd
 import seaborn as sns
-import sklearn
-import sklearn.neighbors
+import sklearn.preprocessing
 from fast_bioservices.pipeline import ensembl_to_gene_id_and_symbol, gene_symbol_to_ensembl_and_gene_id
 from loguru import logger
 from pandas import DataFrame
@@ -51,14 +49,14 @@ class _StudyMetrics:
     study: str
     num_samples: int
     count_matrix: pd.DataFrame
-    fragment_lengths: npt.NDArray[np.float32]
+    fragment_lengths: npt.NDArray[float]
     sample_names: list[str]
     layout: list[LayoutMethod]
-    entrez_gene_ids: list[str]
-    gene_sizes: npt.NDArray[np.float32]
+    entrez_gene_ids: npt.NDArray[int]
+    gene_sizes: npt.NDArray[int]
     __normalization_matrix: pd.DataFrame = field(default_factory=pd.DataFrame)
     __z_score_matrix: pd.DataFrame = field(default_factory=pd.DataFrame)
-    __high_confidence_entrez_gene_ids: list[str] = field(default=list)
+    __high_confidence_entrez_gene_ids: list[str] = field(default_factory=list)
 
     def __post_init__(self):
         for layout in self.layout:
@@ -136,7 +134,7 @@ def genefilter(data: pd.DataFrame | npt.NDArray, filter_func: Callable[[npt.NDAr
     :param filter_func: THe function to filter the data by
     :return: A NumPy array of the filtered data.
     """
-    if not isinstance(data, (pd.DataFrame, npt.NDArray)):
+    if not isinstance(data, pd.DataFrame | npt.NDArray):
         _log_and_raise_error(
             f"Unsupported data type. Must be a Pandas DataFrame or a NumPy array, got '{type(data)}'",
             error=TypeError,
@@ -165,31 +163,29 @@ async def _build_matrix_results(
     conversion["ensembl_gene_id"] = conversion["ensembl_gene_id"].str.split(",")
     conversion = conversion.explode("ensembl_gene_id")
     conversion.reset_index(inplace=True, drop=True)
+    conversion = conversion[conversion["entrez_gene_id"] != "-"]  # drop missing entrez IDs
+    conversion["entrez_gene_id"] = conversion["entrez_gene_id"].astype(int)  # float32 is needed because np.nan is a float
 
-    merge_on = []
-    if "ensembl_gene_id" in matrix.columns and "ensembl_gene_id" in conversion.columns:
-        merge_on.append("ensembl_gene_id")
-    if "entrez_gene_id" in matrix.columns and "entrez_gene_id" in conversion.columns:
-        merge_on.append("entrez_gene_id")
-    if "gene_symbol" in matrix.columns and "gene_symbol" in conversion.columns:
-        merge_on.append("gene_symbol")
-
+    # merge_on should contain at least one of "ensembl_gene_id", "entrez_gene_id", or "gene_symbol"
+    merge_on: list[str] = list(set(matrix.columns).intersection(conversion.columns))
     if not merge_on:
         _log_and_raise_error(
-            "No columns to merge on. Tested 'ensembl_gene_id', 'entrez_gene_id', and 'gene_symbol'. Please check your input files.",
+            (
+                "No columns to merge on, unable to find at least one of `ensembl_gene_id`, `entrez_gene_id`, or `gene_symbol`. "
+                "Please check your input files."
+            ),
             error=ValueError,
             level=LogLevel.ERROR,
         )
+    if "entrez_gene_id" in matrix.columns:
+        matrix["entrez_gene_id"] = matrix["entrez_gene_id"].astype(int)
     matrix = matrix.merge(conversion, on=merge_on, how="left")
 
-    # Only include Entrez and Ensembl Gene IDs that are present in `gene_info`
-    matrix["entrez_gene_id"] = matrix["entrez_gene_id"].str.split("//")
-    matrix = matrix.explode("entrez_gene_id")
-    matrix = matrix.replace(to_replace="-", value=pd.NA).dropna()
-    matrix["entrez_gene_id"] = matrix["entrez_gene_id"].astype(int)
+    # drop rows that have `0` in `entrez_gene_id` column
+    matrix = matrix[matrix["entrez_gene_id"] != 0].reset_index(drop=True, inplace=False)
+    gene_info = gene_info[gene_info["entrez_gene_id"] != 0].reset_index(drop=True, inplace=False)
 
     gene_info = gene_info_migrations(gene_info)
-    gene_info = gene_info.replace(to_replace="-", value=pd.NA).dropna()
     gene_info["entrez_gene_id"] = gene_info["entrez_gene_id"].astype(int)
 
     counts_matrix = matrix.merge(
@@ -204,19 +200,19 @@ async def _build_matrix_results(
         how="inner",
     )
 
-    entrez_gene_ids: list[str] = gene_info["entrez_gene_id"].tolist()
+    entrez_gene_ids: npt.NDArray[int] = gene_info["entrez_gene_id"].to_numpy()
     metrics: NamedMetrics = {}
-    for study in metadata_df["study"].unique().tolist():
+    for study in metadata_df["study"].unique():
         study_sample_names = metadata_df[metadata_df["study"] == study]["sample_name"].tolist()
         layouts = metadata_df[metadata_df["study"] == study]["layout"].tolist()
         metrics[study] = _StudyMetrics(
             count_matrix=counts_matrix[counts_matrix.columns.intersection(study_sample_names)],
-            fragment_lengths=metadata_df[metadata_df["study"] == study]["fragment_length"].values,
+            fragment_lengths=metadata_df[metadata_df["study"] == study]["fragment_length"].values.astype(float),
             sample_names=study_sample_names,
             layout=[LayoutMethod(layout) for layout in layouts],
             num_samples=len(study_sample_names),
             entrez_gene_ids=entrez_gene_ids,
-            gene_sizes=np.array(gene_info["size"].values).astype(np.float32),
+            gene_sizes=gene_info["size"].values.astype(int),
             study=study,
         )
         metrics[study].fragment_lengths[np.isnan(metrics[study].fragment_lengths)] = 0
@@ -241,29 +237,26 @@ def calculate_tpm(metrics: NamedMetrics) -> NamedMetrics:
     return metrics
 
 
-def _calculate_fpkm(metrics: NamedMetrics, scale: int = 1e6) -> NamedMetrics:
+def _calculate_fpkm(metrics: NamedMetrics, scale: float = 1e6) -> NamedMetrics:
     """Calculate the Fragments Per Kilobase of transcript per Million mapped reads (FPKM) for each sample in the metrics dictionary."""
     for study in metrics:
-        matrix_values = []
+        matrix_values: list[npt.NDArray[float]] = []
         for sample in range(metrics[study].num_samples):
             layout = metrics[study].layout[sample]
-            count_matrix: pd.DataFrame = metrics[study].count_matrix.iloc[:, sample].values.astype(np.float32)
-            gene_lengths = (
-                metrics[study].fragment_lengths[sample].astype(np.float32)
-                if layout == LayoutMethod.paired_end
-                else metrics[study].gene_sizes.astype(np.float32)
-            )
+            count_matrix: npt.NDArray[int] = metrics[study].count_matrix.iloc[:, sample].values
+
+            gene_lengths = metrics[study].fragment_lengths[sample] if layout == LayoutMethod.paired_end else metrics[study].gene_sizes.astype(int)
             gene_lengths_kb = gene_lengths / 1000.0
 
             match layout:
                 case LayoutMethod.paired_end:  # FPKM
-                    total_fragments = count_matrix.sum(axis=0)
+                    total_fragments: npt.NDArray[int] = count_matrix.sum(axis=0)
                     if total_fragments == 0:
-                        fragments_per_kilobase_million = np.nan
+                        fragments_per_kilobase_million: float = np.nan
                     else:
-                        counts_per_million = total_fragments / scale
-                        fragments_per_kilobase = count_matrix / gene_lengths_kb
-                        fragments_per_kilobase_million = fragments_per_kilobase / counts_per_million
+                        counts_per_million: npt.NDArray[float] = total_fragments / scale
+                        fragments_per_kilobase: npt.NDArray[float] = count_matrix / counts_per_million
+                        fragments_per_kilobase_million: npt.NDArray[float] = fragments_per_kilobase / gene_lengths_kb
                     matrix_values.append(fragments_per_kilobase_million)
                 case LayoutMethod.single_end:  # RPKM
                     reads_per_kilobase = count_matrix / gene_lengths_kb
@@ -296,7 +289,8 @@ def _calculate_fpkm(metrics: NamedMetrics, scale: int = 1e6) -> NamedMetrics:
 def _zfpkm_calculation(
     column: pd.Series,
     peak_parameters: PeakIdentificationParameters,
-    bandwidth: int = 0.5,
+    bandwidth: float = 1.0,
+    epsilon: float = 1e-10,
 ) -> _ZFPKMResult:
     """Log2 Transformations.
 
@@ -331,7 +325,7 @@ def _zfpkm_calculation(
          - Standard deviation is estimated based on the assumption that the right tail of the distribution
             This represents expressed genes) can be approximated by a half-normal distribution
 
-     zFPKM Transformation
+    zFPKM Transformation
         - Centers disbribution around 0 and scales it by the standard deviation.
             This makes it easier to compare gene expression across different samples
         - Represents the number of standard deviations away from the mean of the "inactive" gene distribution
@@ -340,25 +334,41 @@ def _zfpkm_calculation(
         - Research shows that a zFPKM value of -3 or greater can be used as
             a threshold for calling a gene as "active" and/or "expressed"
             : https://doi.org/10.1186/1471-2164-14-778
-    """
-    values = column.values
-    refit: KernelDensity = KernelDensity(kernel="gaussian", bandwidth=bandwidth).fit(values.reshape(-1, 1))  # type: ignore
 
-    x_range = np.linspace(values.min(), values.max(), 2000)
-    density = np.exp(refit.score_samples(x_range.reshape(-1, 1)))
+    Args:
+          column: A pandas Series representing a single sample's FPKM values.
+          peak_parameters: Parameters for peak identification.
+          bandwidth: The bandwidth for the Kernel Density Estimation (KDE).
+          epsilon: A small value to add to FPKM values to prevent log2-divide-by-0 errors
+
+    Returns:
+            A named tuple containing the zFPKM values, density estimate, mean (mu), standard deviation, and maximum FPKM value.
+
+    """
+    log2values: npt.NDArray[float] = np.log2(column.values + epsilon)
+
+    # 1D KDE *always* has one feature and many samples. scikit-learn expects data in the format of (n_samples, n_features)
+    # Thus, we use `.reshape(-1, 1)` because we know there is a single feature
+    # Even though the data is FPKM of many genes for a single sample, it is still one feature over many samples
+    # `-1` indicates the unknown dimension (the number of samples)
+    # `1` indicates the known dimension (the number of genes [also known as features])
+    # https://scikit-learn.org/stable/auto_examples/neighbors/plot_kde_1d.html#sphx-glr-auto-examples-neighbors-plot-kde-1d-py
+    kde: KernelDensity = KernelDensity(kernel="gaussian", bandwidth=bandwidth).fit(log2values.reshape(-1, 1))
+
+    x_range: npt.NDArray[float] = np.linspace(log2values.min(), log2values.max(), 2000).reshape(-1, 1)
+    density: npt.NDArray[float] = np.exp(kde.score_samples(x_range))
     peaks, _ = find_peaks(density, height=peak_parameters.height, distance=peak_parameters.distance)
     peak_positions = x_range[peaks]
 
     mu = 0
     max_fpkm = 0
     stddev = 1
-
     if len(peaks) != 0:
         mu = peak_positions.max()
         max_fpkm = density[peaks[np.argmax(peak_positions)]]
-        u = values[values > mu].mean()
+        u = log2values[log2values > mu].mean()
         stddev = (u - mu) * np.sqrt(np.pi / 2)
-    zfpkm = pd.Series((values - mu) / stddev, dtype=np.float32, name=column.name)
+    zfpkm = pd.Series((log2values - mu) / stddev, dtype=float, name=column.name)
 
     return _ZFPKMResult(zfpkm=zfpkm, density=Density(x_range, density), mu=mu, std_dev=stddev, max_fpkm=max_fpkm)
 
@@ -366,7 +376,7 @@ def _zfpkm_calculation(
 def zfpkm_transform(
     fpkm_df: pd.DataFrame,
     peak_parameters: PeakIdentificationParameters,
-    bandwidth: int,
+    bandwidth: float,
     update_every_percent: float = 0.1,
 ) -> tuple[dict[str, _ZFPKMResult], DataFrame]:
     """Perform zFPKM calculation/transformation."""
@@ -420,41 +430,36 @@ def zfpkm_transform(
     return results, zfpkm_df
 
 
-def zfpkm_plot(results, *, output_png_filepath: Path, plot_xfloor: int = -4):
+def zfpkm_plot(results: dict[str, _ZFPKMResult], *, output_png_dirpath: Path, plot_xfloor: int = -4, subplot_titles: bool = True) -> None:
     """Plot the log2(FPKM) density and fitted Gaussian for each sample.
 
-    :param results: A dictionary of intermediate results from zfpkm_transform.
-    :param output_png_filepath: Output filepath location
-    :param: subplot_titles: Whether to display facet titles (sample names).
-    :param plot_xfloor: Lower limit for the x-axis.
-    :param subplot_titles: Whether to display facet titles (sample names).
+    Args:
+        results: A dictionary of intermediate results from zfpkm_transform.
+        output_png_dirpath: Output directory location
+        subplot_titles: Whether to display facet titles (sample names).
+        plot_xfloor: Lower limit for the x-axis.
+        subplot_titles: Whether to display facet titles (sample names).
+
     """
     to_concat: list[pd.DataFrame] = [None] * len(results)  # type: ignore  # ignoring because None is not of type pd.DataFrame
     for name, result in results.items():
-        stddev = result.std_dev
-        x = np.array(result.density.x)
-        y = np.array(result.density.y)
-
-        fitted = np.exp(-0.5 * ((x - result.mu) / stddev) ** 2) / (stddev * np.sqrt(2 * np.pi))
-        max_fpkm = y.max()
-        max_fitted = fitted.max()
-        scale_fitted = fitted * (max_fpkm / max_fitted)
-
-        to_concat.append(
-            pd.DataFrame(
-                {
-                    "sample_name": [name] * len(x),
-                    "log2fpkm": x,
-                    "fpkm_density": y,
-                    "fitted_density_scaled": scale_fitted,
-                }
-            )
-        )
+        stddev: float = float(result.std_dev)
+        x: npt.NDArray[float] = result.density.x.flatten()
+        y: npt.NDArray[float] = result.density.y.flatten()
+
+        fitted: npt.NDArray[float] = np.exp(-0.5 * ((x - result.mu) / stddev) ** 2) / (stddev * np.sqrt(2 * np.pi))
+        max_fpkm: float = float(y.max())
+        max_fitted: float = float(fitted.max())
+        scale_fitted: float = fitted * max_fpkm / max_fitted
+        to_concat.append(pd.DataFrame({"sample_name": name, "log2fpkm": x, "fpkm_density": y, "fitted_density_scaled": scale_fitted}))
+
     mega_df = pd.concat(to_concat, ignore_index=True)
     mega_df.columns = pd.Series(data=["sample_name", "log2fpkm", "fpkm_density", "fitted_density_scaled"])
     mega_df = mega_df.melt(id_vars=["log2fpkm", "sample_name"], var_name="source", value_name="density")
 
-    _, axes = plt.subplots(nrows=len(results), ncols=1, figsize=(8, 4 * len(results)))
+    fig: plt.Figure
+    axes: list[plt.Axes]
+    fig, axes = plt.subplots(nrows=len(results), ncols=1, figsize=(8, 4 * len(results)))
     if len(results) == 1:
         axes = [axes]
 
@@ -466,16 +471,17 @@ def zfpkm_plot(results, *, output_png_filepath: Path, plot_xfloor: int = -4):
             group = sample_data[sample_data["source"] == source_type]
             sns.lineplot(data=group, x="log2fpkm", y="density", label=source_type, ax=axis)
 
+        if subplot_titles:
+            axis.set_title(f"Sample: {sample_name}")
         axis.set_xlim(plot_xfloor, sample_data["log2fpkm"].max())
         axis.set_xlabel("log2(FPKM)")
         axis.set_ylabel("density [scaled]")
         axis.legend(title="Source")
 
+    output_png_dirpath.mkdir(parents=True, exist_ok=True)
+    sample_name: str = next(iter(results.keys()))[:-2]  # Go from 'control1hr_S1R1' to 'control1hr_S1'
     plt.tight_layout()
-    if output_png_filepath.suffix != ".png":
-        logger.warning(f"Output filepath did not end in '.png', setting to '.png' now. Got: '{output_png_filepath.suffix}'")
-        output_png_filepath = output_png_filepath.with_suffix(".png")
-    plt.savefig(output_png_filepath)
+    plt.savefig(Path(output_png_dirpath, f"{sample_name}_zfpkm_density.png"))
 
 
 def calculate_z_score(metrics: NamedMetrics) -> NamedMetrics:
@@ -512,7 +518,7 @@ def cpm_filter(
         #   thus, (0 / 1) * 1_000_000 = 0
         library_size[library_size == 0] = 1
 
-        output_filepath = config.result_dir / context_name / prep.value / f"CPM_Matrix_{prep.value}_{sample}.csv"
+        output_filepath = Path(config.result_dir, context_name, prep.value, f"CPM_Matrix_{prep.value}_{sample}.csv")
         output_filepath.parent.mkdir(parents=True, exist_ok=True)
         counts_per_million: pd.DataFrame = (counts / library_size) * 1_000_000
         counts_per_million.insert(0, "entrez_gene_ids", pd.Series(entrez_ids))
@@ -583,8 +589,8 @@ def zfpkm_filter(
     calculate_fpkm: bool,
     force_zfpkm_plot: bool,
     peak_parameters: PeakIdentificationParameters,
-    bandwidth: int,
-    output_png_filepath: Path | None,
+    bandwidth: float,
+    output_png_dirpath: Path | None,
 ) -> NamedMetrics:
     """Apply zFPKM filtering to the FPKM matrix for a given sample."""
     min_sample_expression = filtering_options.replicate_ratio
@@ -606,12 +612,10 @@ def zfpkm_filter(
                 "Not plotting zFPKM results because more than 10 plots would be created. "
                 "If you would like to plot them anyway, set 'force_zfpkm_plot' to True"
             )
-        elif output_png_filepath is None:
+        elif output_png_dirpath is None:
             logger.critical("Output zFPKM PNG filepath is None, set a path to plot zFPKM graphs")
         else:
-            output_png_filepath.parent.mkdir(parents=True, exist_ok=True)
-            output_png_filepath.unlink(missing_ok=True)
-            zfpkm_plot(results, output_png_filepath=output_png_filepath)
+            zfpkm_plot(results, output_png_dirpath=output_png_dirpath)
 
         metric.z_score_matrix = zfpkm_df
 
@@ -619,13 +623,13 @@ def zfpkm_filter(
         min_samples = round(min_sample_expression * len(zfpkm_df.columns))
         min_func = k_over_a(min_samples, cut_off)
         min_genes: npt.NDArray[bool] = genefilter(zfpkm_df, min_func)
-        metric.entrez_gene_ids = [gene for gene, keep in zip(metric.entrez_gene_ids, min_genes, strict=True) if keep]
+        metric.entrez_gene_ids = [gene for gene, keep in zip(zfpkm_df.index, min_genes, strict=True) if keep]
 
         # determine which genes are confidently expressed
         top_samples = round(high_confidence_sample_expression * len(zfpkm_df.columns))
         top_func = k_over_a(top_samples, cut_off)
         top_genes: npt.NDArray[bool] = genefilter(zfpkm_df, top_func)
-        metric.high_confidence_entrez_gene_ids = [gene for gene, keep in zip(metric.entrez_gene_ids, top_genes, strict=True) if keep]
+        metric.high_confidence_entrez_gene_ids = [gene for gene, keep in zip(zfpkm_df.index, top_genes, strict=True) if keep]
 
     return metrics
 
@@ -639,8 +643,8 @@ def filter_counts(
     prep: RNAType,
     force_zfpkm_plot: bool,
     peak_parameters: PeakIdentificationParameters,
-    bandwidth: int,
-    output_png_filepath: Path | None = None,
+    bandwidth: float,
+    output_zfpkm_plot_dirpath: Path | None = None,
 ) -> NamedMetrics:
     """Filter the count matrix based on the specified technique."""
     match technique:
@@ -656,7 +660,7 @@ def filter_counts(
                 force_zfpkm_plot=force_zfpkm_plot,
                 peak_parameters=peak_parameters,
                 bandwidth=bandwidth,
-                output_png_filepath=output_png_filepath,
+                output_png_dirpath=output_zfpkm_plot_dirpath,
             )
         case FilteringTechnique.UMI:
             # UMI filtering is the same as zFPKM filtering without calculating FPKM
@@ -667,7 +671,7 @@ def filter_counts(
                 force_zfpkm_plot=force_zfpkm_plot,
                 peak_parameters=peak_parameters,
                 bandwidth=bandwidth,
-                output_png_filepath=output_png_filepath,
+                output_png_dirpath=output_zfpkm_plot_dirpath,
             )
         case _:
             _log_and_raise_error(
@@ -692,15 +696,16 @@ async def _process(
     cut_off: int | float,
     force_zfpkm_plot: bool,
     peak_parameters: PeakIdentificationParameters,
-    bandwidth: int,
+    bandwidth: float,
     output_boolean_activity_filepath: Path,
     output_zscore_normalization_filepath: Path,
-    output_zfpkm_png_filepath: Path | None,
+    output_zfpkm_plot_dirpath: Path | None,
 ):
     """Save the results of the RNA-Seq tests to a CSV file."""
     output_boolean_activity_filepath.parent.mkdir(parents=True, exist_ok=True)
 
-    rnaseq_matrix: pd.DataFrame = await _read_file(rnaseq_matrix_filepath)
+    rnaseq_matrix: pd.DataFrame = await _read_file(rnaseq_matrix_filepath, h5ad_as_df=True)
+
     if rnaseq_matrix_filepath.suffix == ".h5ad":
         conversion = await gene_symbol_to_ensembl_and_gene_id(symbols=rnaseq_matrix["gene_symbol"].tolist(), taxon=taxon)
         conversion.reset_index(inplace=True)
@@ -723,7 +728,6 @@ async def _process(
     )
 
     metrics = read_counts_results.metrics
-    entrez_gene_ids = read_counts_results.entrez_gene_ids
 
     metrics: NamedMetrics = filter_counts(
         context_name=context_name,
@@ -734,7 +738,7 @@ async def _process(
         force_zfpkm_plot=force_zfpkm_plot,
         peak_parameters=peak_parameters,
         bandwidth=bandwidth,
-        output_png_filepath=output_zfpkm_png_filepath,
+        output_zfpkm_plot_dirpath=output_zfpkm_plot_dirpath,
     )
 
     merged_zscore_df = pd.DataFrame()
@@ -775,12 +779,14 @@ async def _process(
     top_df["prop"] = top_df["frequency"] / len(metrics)
     top_df = top_df[top_df["prop"] >= filtering_options.high_batch_ratio]
 
-    boolean_matrix = pd.DataFrame(data={"entrez_gene_id": entrez_gene_ids, "expressed": 0, "high": 0})
-    for gene in entrez_gene_ids:
-        if gene in expression_df["entrez_gene_id"]:
-            boolean_matrix.loc[gene, "expressed"] = 1
-        if gene in top_df["entrez_gene_id"]:
-            boolean_matrix.loc[gene, "high"] = 1
+    entrez_id_series = pd.Series(read_counts_results.entrez_gene_ids)
+    boolean_matrix = pd.DataFrame(
+        data={
+            "entrez_gene_id": read_counts_results.entrez_gene_ids,
+            "expressed": entrez_id_series.isin(expression_df["entrez_gene_id"]).astype(int),
+            "high": entrez_id_series.isin(top_df["entrez_gene_id"]).astype(int),
+        }
+    )
 
     expressed_count = len(boolean_matrix[boolean_matrix["expressed"] == 1])
     high_confidence_count = len(boolean_matrix[boolean_matrix["high"] == 1])
@@ -801,18 +807,18 @@ async def rnaseq_gen(  # noqa: C901
     output_zscore_normalization_filepath: Path,
     input_metadata_filepath_or_df: Path | pd.DataFrame,
     replicate_ratio: float = 0.5,
-    high_replicate_ratio: float = 1.0,
+    high_replicate_ratio: float = 0.8,
     batch_ratio: float = 0.5,
-    high_batch_ratio: float = 1.0,
+    high_batch_ratio: float = 0.75,
     technique: FilteringTechnique | str = FilteringTechnique.ZFPKM,
     zfpkm_peak_height: float = 0.02,
     zfpkm_peak_distance: float = 1.0,
-    zfpkm_bandwidth: int = 1,
+    zfpkm_bandwidth: float = 1.0,
     cutoff: int | float | None = None,
     force_zfpkm_plot: bool = False,
     log_level: LogLevel = LogLevel.INFO,
-    log_location: str | TextIOWrapper = sys.stderr,
-    output_zfpkm_png_filepath: Path | None = None,
+    log_location: str | TextIO = sys.stderr,
+    output_zfpkm_plot_dirpath: Path | None = None,
 ) -> None:
     """Generate a list of active and high-confidence genes from a gene count matrix.
 
@@ -843,7 +849,7 @@ async def rnaseq_gen(  # noqa: C901
     :param force_zfpkm_plot: If too many samples exist, should plotting be done anyway?
     :param log_level: The level of logging to output
     :param log_location: The location to write logs to
-    :param output_zfpkm_png_filepath: Optional filepath to save zFPKM plots
+    :param output_zfpkm_plot_dirpath: Optional filepath to save zFPKM plots
     :return: None
     """
     _set_up_logging(level=log_level, location=log_location)
@@ -851,7 +857,7 @@ async def rnaseq_gen(  # noqa: C901
     technique = FilteringTechnique(technique) if isinstance(technique, str) else technique
     match technique:
         case FilteringTechnique.TPM:
-            cutoff = cutoff or 25
+            cutoff: int | float = cutoff or 25
             if cutoff < 1 or cutoff > 100:
                 _log_and_raise_error(
                     "Quantile must be between 1 - 100",
@@ -870,7 +876,7 @@ async def rnaseq_gen(  # noqa: C901
                 cutoff = "default"
 
         case FilteringTechnique.ZFPKM | FilteringTechnique.UMI:
-            cutoff = cutoff or -3
+            cutoff: int | float = cutoff or -3
         case _:
             _log_and_raise_error(
                 f"Technique must be one of {','.join(FilteringTechnique)}. Got: {technique.value}",
@@ -936,5 +942,5 @@ async def rnaseq_gen(  # noqa: C901
         bandwidth=zfpkm_bandwidth,
         output_boolean_activity_filepath=output_boolean_activity_filepath,
         output_zscore_normalization_filepath=output_zscore_normalization_filepath,
-        output_zfpkm_png_filepath=output_zfpkm_png_filepath,
+        output_zfpkm_plot_dirpath=output_zfpkm_plot_dirpath,
     )
diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 41b8d971..1d73a5e8 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -8,7 +8,7 @@
 from io import StringIO, TextIOWrapper
 from itertools import chain
 from pathlib import Path
-from typing import Final, Literal
+from typing import Final, Literal, cast
 
 import aiofiles
 import numpy as np
@@ -418,13 +418,16 @@ async def _create_config_df(  # noqa: C901
 
         fragment_label = f"{context_name}_{label}_fragment_size.txt"
         frag_paths = [p for p in aux_lookup["fragment"].values() if p.name == fragment_label]
-        if not frag_paths and prep != RNAType.TRNA.value:
+        if not frag_paths and prep.lower() != RNAType.TRNA.value.lower():
             logger.warning(f"No fragment file for '{label}'; defaulting to 100 bp (needed for zFPKM).")
             mean_frag = 100.0
         elif len(frag_paths) == 1 and layout == "single-end":
             mean_frag = 0.0
         else:  # 1-N files, paired end
-            dfs: list[pd.DataFrame] = await asyncio.gather(*[_read_file(f, sep="\t", on_bad_lines="skip") for f in frag_paths])
+            dfs: list[pd.DataFrame] = cast(
+                typ=list[pd.DataFrame],
+                val=await asyncio.gather(*[_read_file(f, sep="\t", on_bad_lines="skip") for f in frag_paths]),
+            )
             for df in dfs:
                 df["meanxcount"] = df["frag_mean"] * df["frag_count"]
                 counts = np.array([df["frag_count"].sum() for df in dfs])
diff --git a/main/como/sanity_checks/__init__.py b/main/como/sanity_checks/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/main/como/sanity_checks/cobra_sanity_check.py b/main/como/sanity_checks/cobra_sanity_check.py
new file mode 100644
index 00000000..fdd9fc28
--- /dev/null
+++ b/main/como/sanity_checks/cobra_sanity_check.py
@@ -0,0 +1,366 @@
+"""Sanity check from COBRA Toolbox.
+
+The original code is available at: https://opencobra.github.io/cobratoolbox/stable/tutorials/tutorial_modelSanityChecks.html
+Initially written in MATLAB, this is the translated version written in Python.
+
+Checks performed:
+1. leak test
+2. production of protons from nothing as well as from water, and/or oxygen alone
+3. production of matter when atp hydrolysis reaction is allowed to work but all uptakes are closed
+4. production of too much ATP from glucose under aerobic condition
+5. duplicated reactions
+6. empty columns in the model.rxnGeneMat
+7. the single gene deletion analysis runs smoothly
+8. ATP yield fro different carbon sources
+9. metabolic objective functions
+10. flux consistency
+11. demand reactions with negative lower bound (should not occur based on definition of demand reactions)
+12. consistency of model.rev which defines reaction reversibility, and the set values for the lower bounds on reactions
+"""
+
+import re
+from pathlib import Path
+from typing import Literal
+
+import cobra
+import cobra.util.array
+import numpy as np
+import numpy.typing as npt
+from loguru import logger
+from scipy.sparse import csc_matrix
+
+from como.sanity_checks.fast_leak_test import fast_leak_test
+
+# from cobra import Metabolite, Reaction
+# from cobra.flux_analysis import single_gene_deletion
+
+
+def create_metabolite_if_not_exists(model: cobra.Model, met_id: str, name: str, compartment: str) -> cobra.Metabolite:
+    """Validate that a metabolite exists in a given model.
+
+    Args:
+        model: The model to check
+        met_id: The metabolite ID to check
+        name: The name of the metabolite to add if it does not exist
+        compartment: The compartment of the metabolite to add if it does not exist
+
+    Returns:
+        The metabolite object, either existing or newly created.
+
+    """
+    if met_id in model.metabolites:
+        return model.metabolites.get_by_id(met_id)  # type: ignore
+    else:
+        met = cobra.Metabolite(id=met_id, name=name, compartment=compartment)
+        model.add_metabolites([met])
+        return met
+
+
+def preprocess(model_path: Path, solver: Literal["glpk", "cplex", "gurobi"] = "glpk") -> tuple[cobra.Model, list[str]]:  # noqa: C901
+    """Close the given model.
+
+    Args:
+        model_path: Path to the SBML model file
+        solver: The solver to use. Options are "glpk", "cplex", or "gurobi". Default is "glpk".
+
+    Returns:
+        A tuple containing the closed model and a list of selected exchange reactions.
+
+    Raises:
+        FileNotFoundError: If the model file does not exist.
+
+    """
+    if not model_path.exists():
+        raise FileNotFoundError(f"File {model_path} does not exist")
+
+    model: cobra.Model = cobra.io.read_sbml_model(model_path)
+
+    # Change solver if necessary
+    model.solver = solver
+
+    #  Replace reaction abbreviation for the ATP hydrolysis (DM_atp_c_) and Biomass reaction used differently in various models.
+    if "ATPM" in model.reactions:
+        model.reactions.get_by_id("ATPM").id = "DM_atp[c]"
+    if "ATPhyd" in model.reactions:
+        model.reactions.get_by_id("ATPhyd").id = "DM_atp[c]"
+    if "DM_atp(c)" in model.reactions:
+        model.reactions.get_by_id("DM_atp(c)").id = "DM_atp[c]"
+    if "EX_biomass_reaction" in model.reactions:
+        model.reactions.get_by_id("EX_biomass_reaction").id = "biomass_reaction"
+    if "EX_biomass_maintenance" in model.reactions:
+        model.reactions.get_by_id("EX_biomass_maintenance").id = "biomass_maintenance"
+    if "EX_biomass_reaction_noTrTr" in model.reactions:
+        model.reactions.get_by_id("EX_biomass_reaction_noTrTr").id = "biomass_maintenance_noTrTr"
+
+    if "DM_atp[c]" not in model.reactions:
+        # Make sure ATP, ADP, H2O, H+ and Pi exist or are added
+
+        atp: cobra.Metabolite = create_metabolite_if_not_exists(model=model, met_id="atp_c", name="ATP", compartment="c")
+        adp: cobra.Metabolite = create_metabolite_if_not_exists(model=model, met_id="adp_c", name="ADP", compartment="c")
+        pi: cobra.Metabolite = create_metabolite_if_not_exists(model=model, met_id="pi_c", name="Phosphate", compartment="c")
+        h2o: cobra.Metabolite = create_metabolite_if_not_exists(model=model, met_id="h2o_c", name="H2O", compartment="c")
+        h: cobra.Metabolite = create_metabolite_if_not_exists(model=model, met_id="h_c", name="Proton", compartment="c")
+
+        atp_hydrolysis = cobra.Reaction("DM_atp[c]")
+        atp_hydrolysis.name = "ATP maintenance reaction"
+        atp_hydrolysis.lower_bound = 0
+        atp_hydrolysis.upper_bound = 1000
+        atp_hydrolysis.add_metabolites({adp: 1.0, atp: -1.0, h: 1.0, h2o: -1.0, pi: 1.0})
+        model.add_reactions([atp_hydrolysis])
+
+    # === Add water exchange reaction if missing ===
+    if "EX_h2o[e]" not in model.reactions:
+        h2o_e = create_metabolite_if_not_exists(model=model, met_id="h2o_e", name="H2O", compartment="e")
+        model.add_boundary(h2o_e, type="exchange", reaction_id="EX_h2o[e]")
+
+    # Set lower bound of the biomass reaction to 0.
+    if "biomass_maintenance" in model.reactions:
+        model.reactions.get_by_id("biomass_maintenance").lower_bound = 0  # type: ignore
+
+    # Harmonize different use of brackets.
+    for rxn in model.reactions:
+        new_id = rxn.id
+        new_id = new_id.replace("(", "[")
+        new_id = new_id.replace(")", "]")
+        new_id = new_id.replace("-", "_")
+        new_id = re.sub(r"^Ex_", "EX_", new_id)
+        new_id = re.sub(r"^(Sink|sink|SK)_", "sink_", new_id)
+        rxn.id = new_id
+
+    model_closed = model.copy()
+
+    # Step 1: Collect reaction IDs by regex name patterns
+    # match 'Ex_', 'EX_', 'DM_', or 'sink_' in the reaction id and add matched reaction ids in a list
+    modelexchanges_ids: list[str] = [rxn.id for rxn in model_closed.reactions if re.search(pattern=r"^(EX|DM|sink)_", string=rxn.id)]
+
+    # Step 2: Matrix-based method to find selected_exchanges reactions (single metabolite, 1 entry)
+    s_matrix: npt.NDArray[np.floating] = cobra.util.array.create_stoichiometric_matrix(model_closed)
+
+    # Step 2: Convert it to a sparse CSC matrix to optimize memory usage and performance
+    s_sparse = csc_matrix(s_matrix)
+    abs_s = abs(s_sparse)
+
+    selected_exchanges: list[str] = [
+        model_closed.reactions[j].id for j in range(s_sparse.shape[1]) if np.sum(abs_s[:, j] == 1) == 1 and np.sum(s_sparse[:, j] != 0) == 1
+    ]
+
+    # Step 3: Combine and deduplicate all identified exchange-like reactions
+    modelexchanges: list[str] = sorted(set(modelexchanges_ids + selected_exchanges))
+
+    # Step 4: Set bounds for these reactions
+    try:
+        for rxn_id in modelexchanges:
+            model_closed.reactions.get_by_id(rxn_id).bounds = (0, 1000)  # type: ignore
+    except KeyError:
+        logger.warning(f"Reaction {rxn_id} not found in model.")
+
+    # Optional: Save original model copy
+    model_closed_original = model_closed
+
+    logger.success("Model preprocessed!")
+    return model_closed_original, selected_exchanges
+
+
+def run_sanity_checks(model_closed: cobra.Model, selected_exchanges: list[str]):  # noqa: C901
+    """Run the MATLAB sanity checks.
+
+    Args:
+        model_closed: The closed model from `preprocess`
+        selected_exchanges: The selected exchanges from `preprocess`
+
+    """
+    table_checks = []
+    tol = 1e-6
+
+    # Perform leak test, i.e., whether the closed model can produce any exchanged metabolite, as defined in the model, from nothing.
+    with model_closed as model_copy:
+        leak_reactions, _, _ = fast_leak_test(
+            model_copy,
+            [rxn.id for rxn in model_copy.reactions if rxn.id in selected_exchanges],
+            demand_test=False,
+        )
+        table_check_row = ["fastLeakTest1"]
+        if len(leak_reactions) > 0:
+            logger.warning("Model leaks metabolites!")
+            table_check_row.append("Model leaks metabolites!")
+        else:
+            table_check_row.append("Leak free!")
+        table_checks.append(table_check_row)
+
+    # Test if something leaks when demand reactions for each metabolite in the model are added. Note that this step is time consuming.
+    with model_closed as model_copy:
+        table_checks = []
+        leak_rxn_demand, _, _ = fast_leak_test(model_copy, [rxn.id for rxn in model_copy.reactions if rxn.id in selected_exchanges], demand_test=True)
+        table_check_row = ["fastLeakTest 2 - add demand reactions for each metabolite in the model"]
+        if len(leak_rxn_demand) > 0:
+            table_check_row.append("Model leaks metabolites when demand reactions are added!")
+        else:
+            table_check_row.append("Leak free when demand reactions are added!")
+        table_checks.append(table_check_row)
+
+    # Test if the model produces energy from water
+    with model_closed as model_copy:
+        table_checks = []
+        model_copy.objective = "DM_atp[c]"
+        model_copy.reactions.get_by_id("DM_atp[c]").lower_bound = 0  # type: ignore
+        model_copy.reactions.get_by_id("EX_h2o[e]").lower_bound = -1  # type: ignore
+
+        solution: cobra.Solution = model_copy.optimize()
+        table_check_row = ["EExchanges, sinks, and demands have lb = 0, except h2o"]
+        if abs(solution.objective_value) > tol:
+            table_check_row.append("model produces energy from water!")
+        else:
+            table_check_row.append("model DOES NOT produce energy from water!")
+        table_checks.append(table_check_row)
+
+    # Test if the model produces energy from water and oxygen
+    with model_closed as model_copy:
+        table_checks = []
+        model_copy.objective = "DM_atp[c]"
+        model_copy.reactions.get_by_id("DM_atp[c]").lower_bound = 0  # type: ignore
+        model_copy.reactions.get_by_id("EX_h2o[e]").lower_bound = -1  # allowing water to come in # type: ignore
+        model_copy.reactions.get_by_id("EX_o2[e]").lower_bound = -1  # allowing oxygen to come in # type: ignore
+
+        solution: cobra.Solution = model_copy.optimize()
+        table_check_row = ["Exchanges,sinks, and demands have lb = 0, except h2o and o2"]
+        if abs(solution.objective_value) > tol:
+            table_check_row.append("model produces energy from water and oxygen!")
+        else:
+            table_check_row.append("model DOES NOT produce energy from water and oxygen!")
+        table_checks.append(table_check_row)
+
+    # Test if the model produces matter when atp demand is reversed
+    with model_closed as model_copy:
+        model_copy.objective = "DM_atp[c]"
+        model_copy.reactions.get_by_id("DM_atp[c]").lower_bound = -1000  # type: ignore
+        solution = model_copy.optimize()
+        table_check_row = ["Exchanges, sinks, and demands have lb = 0, allow DM_atp_c_ to be reversible"]
+        if abs(solution.objective_value) > tol:
+            table_check_row.append("model produces no matter when atp demand is reversed!")
+        else:
+            table_check_row.append("model DOES NOT produce no matter when atp demand is reversed!")
+        table_checks.append(table_check_row)
+
+    # Test if the model has flux through h[m] demand
+    with model_closed as model_copy:
+        model_copy.add_reactions(
+            [
+                cobra.Reaction(id="DM_h[m]", name="DM_h[m]", upper_bound=1000)  # lower bound not specified in original code
+            ]
+        )
+        solution = model_copy.optimize()
+        table_check_row = ["Exchanges, sinks, and demands have lb = 0, test flux through DM_h[m] (max)"]
+        if abs(solution.objective_value) > tol:
+            table_check_row.append("model has flux through DM_h[m] (max)!")
+        else:
+            table_check_row.append("model has NO flux through DM_h[m] (max)!")
+        table_checks.append(table_check_row)
+
+    # Test if the  model has flux through h[c] demand
+    with model_closed as model_copy:
+        model_copy.add_reactions(
+            [
+                cobra.Reaction(id="DM_h[c]", name="DM_h[c]", upper_bound=1000)  # lower bound not specified in original code
+            ]
+        )
+        solution: cobra.Solution = model_copy.optimize()
+        table_check_row = ["Exchanges, sinks, and demands have lb = 0, test flux through DM_h[c] (max)"]
+        if abs(solution.objective_value) > tol:
+            table_check_row.append("model has flux through DM_h[c] (max)!")
+        else:
+            table_check_row.append("model has NO flux through DM_h[c] (max)!")
+        table_checks.append(table_check_row)
+
+    # Test if the  model produces too much atp demand from glucose under aerobic condition.
+    # Also consider using the tutorial testModelATPYield to test if the correct ATP yield from different carbon sources can be realized by the model.
+    with model_closed as model_copy:
+        model_copy.objective = "DM_atp[c]"
+        model_copy.reactions.get_by_id("EX_o2[e]").lower_bound = -1000  # type: ignore
+        model_copy.reactions.get_by_id("EX_h2o[e]").lower_bound = -1000  # type: ignore
+        model_copy.reactions.get_by_id("EX_h2o[e]").upper_bound = 1000  # type: ignore
+        model_copy.reactions.get_by_id("EX_co2[e]").upper_bound = 1000  # type: ignore
+        original_sol: cobra.Solution = model_closed.optimize()
+
+        table_check_row = ["ATP yield"]
+        if abs(original_sol.objective_value) > 31:  # this is the theoretical value
+            table_check_row.append("model produces too much atp demand from glc!")
+        else:
+            table_check_row.append("model DOES NOT produce too much atp demand from glc!")
+        table_checks.append(table_check_row)
+
+    # Test metabolic objective functions with open sinks. Note this step is time consuming and may only work.
+
+    # Test metabolic objective functions with closed sinks (lb). Note this step is time consuming and may only work.
+
+    # Compute ATP yield. This test is identical to the material covered in the tutorial testModelATPYield.
+
+    # Check for duplicated reactions in the model.
+    with model_closed as model_copy:
+        table_check_row = ["Check duplicated reactions"]
+        logger.debug("Checking for duplicated reactions...")
+        all_reactions = model_copy.reactions
+        for rxn1 in range(len(all_reactions)):  # print only first 20 for brevity
+            for rxn2 in range(rxn1 + 1, len(all_reactions)):
+                if all_reactions[rxn1].id == all_reactions[rxn2].id:
+                    table_check_row.append(f"There exists a duplicated reaction: {all_reactions[rxn1].id}")
+        table_checks.append(table_check_row)
+        logger.info("Finished checking for duplicated reactions!")
+
+    # Check empty columns in 'model.genes'.
+    with model_closed as model_copy:
+        table_check_row = ["Check empty columns in model_copy.genes"]
+        all_genes = model_copy.genes
+        for gene in all_genes:
+            if gene is None:
+                table_check_row.append("There is an empty column in model_copy.genes")
+        table_checks.append(table_check_row)
+
+    # Check that demand reactions have a lb >= 0.
+    with model_closed as model_copy:
+        table_check_row = ["Check that demand reactions have lb >= 0"]
+        for rxn in model_copy.reactions:
+            reaction_id = rxn.id
+            if (re.search(r"\bDM_", reaction_id)) and rxn.lower_bound < 0:
+                table_check_row.append(f"Demand reaction can have flux in backward direction. {rxn.id}")
+        table_checks.append(table_check_row)
+
+    # Check whether singleGeneDeletion runs smoothly.
+    with model_closed as model_copy:
+        table_check_row = ["Check whether singleGeneDeletion runs smoothly"]
+        try:
+            cobra.flux_analysis.single_gene_deletion(model_copy)
+            table_check_row.append("singleGeneDeletion finished without problems")
+        except Exception:
+            table_check_row.append("There are problems with singleGeneDeletion.")
+        table_checks.append(table_check_row)
+
+    # Check for flux consistency.
+    with model_closed as model_copy:
+        table_check_row = ["Check for flux consistency"]
+        # fastcc requires a numeric stoichiometric matrix and reaction list
+        consistent_submodel = cobra.flux_analysis.fastcc(model_copy, 1e-4)
+
+        if len(consistent_submodel.reactions) == len(model_copy.reactions):
+            table_check_row.append("Model is flux consistent.")
+        else:
+            table_check_row.append("Model is NOT flux consistent.")
+
+        table_checks.append(table_check_row)
+        for row in table_checks:
+            if len(row) >= 2:
+                logger.debug(f"{row[0]:<60} {row[1]}")
+            else:
+                logger.debug(row)
+        original_rxns = {r.id for r in model_copy.reactions}
+        consistent_rxns = {r.id for r in consistent_submodel.reactions}
+        inconsistent_rxns = original_rxns - consistent_rxns
+
+        logger.info(f"Inconsistent reactions ({len(inconsistent_rxns)}):")
+        logger.info(f"original reactions: {len(original_rxns)}")
+
+
+if __name__ == "__main__":
+    model_path = Path("/Users/satominakamura/Desktop/Dr.Helikar Lab/HealthyNaiveB.xml")  # Path to XML file
+
+    model_closed, selected_exchanges = preprocess(model_path)
+    run_sanity_checks(model_closed, selected_exchanges)
diff --git a/main/como/sanity_checks/fast_leak_test.py b/main/como/sanity_checks/fast_leak_test.py
new file mode 100644
index 00000000..0ce92c1a
--- /dev/null
+++ b/main/como/sanity_checks/fast_leak_test.py
@@ -0,0 +1,155 @@
+"""Perform a Fast Leak Test.
+
+This is a fast leak test originally written in MATLAB.
+Source: https://github.com/opencobra/cobratoolbox/blob/950f40cff4256d738fc8421a6ecfe91e084f46dd/src/reconstruction/modelGeneration/fastLeakTest.m
+
+Input
+    model: model structure
+    testRxns: List of exchange reactions to be tested for leaks
+    demandTest (optional): if 'TRUE' is entered, demand reactions for all metabolites in the model are created
+
+Output
+    LeakMets: List of exchange reactions for leaking metabolites
+    modelClosed: Model structure that has been tested for leaks
+    FLuxExV: Flux vector for computed exchange reactions in the closed model
+"""
+
+from collections.abc import Iterable
+
+import cobra
+import numpy as np
+from cobra.util.array import create_stoichiometric_matrix
+from loguru import logger
+from scipy.sparse import csc_matrix
+
+
+def fast_leak_test(
+    model: cobra.Model,
+    test_reactions: Iterable[str],
+    demand_test,
+    tolerance: np.float16 = 1e-6,
+):
+    """Run a Fast Leak Test.
+
+    This is a fast leak test originally written in MATLAB.
+    Source: https://github.com/opencobra/cobratoolbox/blob/950f40cff4256d738fc8421a6ecfe91e084f46dd/src/reconstruction/modelGeneration/fastLeakTest.m
+
+    Args:
+        model: The model to test
+        test_reactions: List of exchange reactions to be tested for leaks
+        demand_test: If 'TRUE', demand reactions for all metabolites in the model are created
+        tolerance: Tolerance level for determining significant flux
+
+    """
+    model_closed = model.copy()
+
+    # Get stoichiometric matrix in CSC format
+    # Step 1: Get a dense NumPy matrix
+    s_dense = create_stoichiometric_matrix(model_closed)
+    # Step 2: Convert it to a sparse CSC matrix
+    s_sparse = csc_matrix(s_dense)
+
+    rxns: list[cobra.Reaction] = list(model_closed.reactions)
+    num_rxns = len(rxns)
+    # Count non-zero stoichiometric entries per reaction (i.e., columns)
+    num_nonzero_per_rxn = np.array((s_sparse != 0).sum(axis=0)).flatten()
+
+    # Generate boolean masks (same as MATLAB logic)
+    exp = np.array([(num_nonzero_per_rxn[i] == 1 and rxns[i].lower_bound >= 0) for i in range(num_rxns)])
+    upt = np.array([(num_nonzero_per_rxn[i] == 1 and rxns[i].upper_bound <= 0) for i in range(num_rxns)])
+    reversible_exchanges = np.array([(num_nonzero_per_rxn[i] == 1 and rxns[i].lower_bound < 0 < rxns[i].upper_bound) for i in range(num_rxns)])
+
+    count = exp | upt | reversible_exchanges
+    # Get the reactions where count[i] is True
+    exchange_rxns = [rxn for i, rxn in enumerate(model_closed.reactions) if count[i]]
+    # Set lower_bound = 0 for those reactions
+    for i in range(num_rxns):
+        if count[i]:
+            rxns[i].lower_bound = 0
+    model_exchange_abbr = list(set(test_reactions).union([rxn.id for rxn in exchange_rxns]))
+
+    # test for all demand reactions is an option
+    if demand_test:  # add demand reactions for all metabolites in model to check for those too
+        rxn_names = []
+        for metabolite in model_closed.metabolites:
+            demand_rxn = add_demand_reaction(model_closed, metabolite)
+            if demand_rxn is not None:
+                rxn_names.append(demand_rxn.id)  # collect demand reaction IDs
+        # return model_closed (and reaction names in the model??)
+    else:
+        rxn_names = []
+
+    model_exchange_abbr = list(set(model_exchange_abbr).union(rxn_names))
+    flux_ex_v = []
+
+    # set only the first reaction on the list as the objective (like how it behaves in the original MATLAB code)
+    flux_exchange = []
+    while model_exchange_abbr:
+        rxn_id = model_exchange_abbr[0]  # Take the first one
+        model_closed.objective = model_closed.reactions.get_by_id(rxn_id)  # Set it as objective
+
+        solution: cobra.Solution = model_closed.optimize()
+        logger.debug(f"Testing exchange: {rxn_id}")
+        logger.debug(f"Objective value: {solution.objective_value}")
+        logger.debug(f"Remaining: {len(model_exchange_abbr)}")
+
+        if solution.status == "infeasible":
+            logger.info("Trivial solution is not a solution of the model.\n Check that you are not enforcing flux...")
+        elif solution.status != "optimal":
+            logger.info("Model has issues (e.g., unbounded). Try adjusting bounds.")
+
+        if solution.objective_value >= tolerance:
+            flux_exchange.append(rxn_id)
+            flux_ex_v.append(solution.objective_value)
+
+        # Remove from the list whether it leaked or not (just like MATLAB logic)
+        model_exchange_abbr.pop(0)
+
+    leak_mets = flux_exchange
+
+    return [leak_mets, model_closed, flux_ex_v]
+
+
+def add_demand_reaction(
+    model: cobra.Model,
+    metabolite: cobra.Metabolite,
+    demand_id: str | None = None,
+    demand_name: str | None = None,
+    lower_bound: int = 0,
+    upper_bound: int = 1000,
+    consumption_rate: int = -1,
+) -> cobra.Reaction:
+    """Add a demand reaction to a given model.
+
+    Args:
+        model: The model to which the demand reaction will be added.
+        metabolite: The metabolite for the demand reaction.
+        demand_id: Optional ID for the demand reaction. If None, it will be generated as 'DM_{metabolite.id}'.
+        demand_name: Optional name for the demand reaction. If None, it will be 'Demand for {metabolite.id}'.
+        lower_bound: Lower bound for the demand reaction.
+        upper_bound: Upper bound for the demand reaction.
+        consumption_rate: The rate at which the metabolite is consumed.
+
+    Returns:
+        The demand reaction from the model (if it exists), or the newly created demand reaction.
+
+    """
+    demand_id: str = demand_id or f"DM_{metabolite.id}"
+    demand_name: str = demand_name or f"Demand for {metabolite.id}"
+
+    # Check if demand reaction already exists
+    if demand_id in model.reactions:
+        logger.info(f"Ignoring reaction '{demand_id}' since it already exists.")
+        return model.reactions.get_by_id(demand_id)  # type: ignore
+
+    # Create a new reaction
+    demand_rxn: cobra.Reaction = cobra.Reaction(
+        id=demand_id,
+        name=demand_name,
+        lower_bound=lower_bound,
+        upper_bound=upper_bound,
+    )
+
+    demand_rxn.add_metabolites({metabolite: consumption_rate})
+    model.add_reactions([demand_rxn])
+    return demand_rxn
diff --git a/main/como/sanity_checks/naiveb_behavior_tests.py b/main/como/sanity_checks/naiveb_behavior_tests.py
new file mode 100644
index 00000000..480b258c
--- /dev/null
+++ b/main/como/sanity_checks/naiveb_behavior_tests.py
@@ -0,0 +1,202 @@
+# ruff: noqa: T201
+"""COMO integration tests.
+
+The aim of these tests are to validate COMO's behavior on file changes.
+
+
+Seven behaviors exist in this file:
+1. Validate that glucose restriction has no impact on B cell function.
+2. Glucose distributes through G6P, F16BP, G3P, and 3PG. Check if the reactions exist in the model.
+3. Naive B cells produce lactate.
+4. In naive B cells, citrate, glutamate, glutamine, å-ketoglutarate, succinate, fumarate, malate are active.
+5. Naive B cells showed no significant increase in the ECAR in response to the glucose addition.
+6. B cell survival requires proper maintenance of glycolytic and oxidative glucose pathways.
+7. Naive B cells seem to rely on FA as their main fuel.
+"""
+
+import csv
+from pathlib import Path
+
+import cobra
+from cobra.flux_analysis import moma, single_reaction_deletion
+
+# The aim of this unit test is to check if the current COMO version (after transition from R to Python)
+# There are seven cell behaviors in total in the unit test
+
+
+def glucose_restriction(model: cobra.Model):
+    """Validate that glucose restriction has no impact on B cell function.
+
+    Args:
+        model: the model to open
+
+    """
+    with Path("glucose_validation.csv").open("w", encoding="utf-8") as o_stream:
+        csv_writer = csv.DictWriter(f=o_stream, fieldnames=["lower_bound", "biomass"])
+        csv_writer.writeheader()
+
+        modified_lb = -1000
+        while modified_lb < 0:
+            for reaction in model.reactions:
+                model.reactions.get_by_id(reaction.id).lower_bound = -1000  # type: ignore
+                model.reactions.get_by_id(reaction.id).upper_bound = 1000  # type: ignore
+                if reaction.id == "EX_glc_D_e":
+                    model.reactions.get_by_id(reaction.id).lower_bound = modified_lb  # type: ignore
+
+                solution_after: cobra.Solution = model.optimize()
+                csv_writer.writerows([{"lower_bound": modified_lb, "biomass": solution_after.objective_value}])
+                modified_lb += 20
+                print(f"lower_bound = {modified_lb}")
+                break
+
+
+def glucose_distribution(model: cobra.Model):
+    """Validate that glucose distributes through G6P, F16BP, G3P, and 3PG."""
+    r_g6p = ["pgi", "pgmt"]
+    r_f16bp = ["galor", "galtt", "ex_galt"]
+    r_g3p = ["tpi", "fba", "gapd"]
+    r_3pg = ["dpgase", "acyp", "pgk", "pgm"]
+    como_model = model
+    glc_dist = [r_g6p, r_f16bp, r_g3p, r_3pg]
+    total_reaction = sum(len(x) for x in glc_dist)
+    num_reaction = 0
+    for reaction in como_model.reactions:
+        i = 0
+        while i < len(glc_dist):
+            for r in glc_dist[i]:
+                if r in reaction.id.lower():
+                    num_reaction += 1
+                    glc_dist[i].remove(r)
+                    # print(f'{r} found in model')
+                else:
+                    continue
+            i += 1
+    if num_reaction == total_reaction:
+        print("All reactions found in model")
+    else:
+        print("Not all reactions found in model")
+
+
+def lactate_production(model: cobra.Model):
+    """Validate that naive B cells produce lactate.
+
+    Naive B cells produce lactate. Disrupt the following reaction:
+    Define a sink reaction for lactose and determine how much metabolite is flowing through the pathways.
+    """
+    with model as model_copy:
+        initial_solution = model_copy.optimize()
+        print(initial_solution.objective_value)
+
+        model_copy.add_boundary(metabolite=model_copy.metabolites.get_by_id("lac_L"), type="sink")  # type: ignore
+        rxn: cobra.Reaction = model.reactions.get_by_id("EX_lac_D[e]")  # type: ignore
+        rxn.knock_out()
+
+        model.objective = "SK_lac_L[c]"
+        solution_after = model.optimize()
+        print(solution_after.objective_value)
+
+
+def citric_acid_cycle_activation(model: cobra.Model):
+    """Validate that Naive B cells activate the citric acid cycle.
+
+    In naive B cells, citrate, glutamate, glutamine, å-ketoglutarate, succinate, fumarate, malate are active
+    Confirm with escher map
+    """
+    metabolites = [
+        "cit_c",
+        "glu_L_c",
+        "gln_L_c",
+        "akg_c",
+        "succ_c",
+        "fum_c",
+        "mal_L_c",
+    ]
+    for met in metabolites:
+        reaction_list: list[str] = []
+
+        for reaction in model.reactions:
+            reaction: cobra.Reaction
+
+            for metabolite in reaction.metabolites:
+                metabolite: cobra.Metabolite
+
+                if metabolite.id == met:
+                    reaction_list.append(reaction.id)
+
+        print(f"Metabolite: {met}, Reactions: {reaction_list}")
+
+
+def ecar_response_with_glucose_addition(model: cobra.Model):
+    """Validate that Naive B cells show no significant response in Extracellular Acidification Rate with glucose addition.
+
+    Untreated or anti-IgM treated B cells showed no significant increase in the ECAR in response to the glucose addition
+    Define a sink reaction for lactose and maximize the exchange reaction of glucose. If the model agrees with the
+    verification, the lactose present in the pathway should increase as well
+    """
+    with model as model_copy:
+        model_copy.add_boundary(metabolite=model_copy.metabolites.get_by_id("lac_L"), type="sink")  # type: ignore
+
+        model_copy.add_boundary(model_copy.metabolites.get_by_id("lac_L[c]"), type="sink")  # type: ignore
+        rxn: cobra.Reaction = model_copy.reactions.get_by_id("EX_lac_D[e]")  # type: ignore
+        rxn.knock_out()
+
+        solution_before = model_copy.optimize()
+        print("Before", solution_before.objective_value)
+
+        glucose_rxn: cobra.Reaction = model_copy.reactions.get_by_id("EX_glc_D[e]")  # type: ignore
+        glucose_rxn.lower_bound = -1000
+        glucose_rxn.upper_bound = 1000
+
+        model_copy.objective = "SK_lac_L[c]"
+        solution_after = model_copy.optimize()
+        print("After", solution_after.objective_value)
+
+
+def glycolysis_and_oxidative_glucose_pathway_maintenance(model: cobra.Model):
+    """Validate that Naive B cells require glycolytic and oxidative glucose pathway maintenance.
+
+    B cell survival requires proper maintenance of glycolytic and oxidative glucose pathways.
+    Use the moma method instead of flux balance analysis and delete one reaction at a time.
+    The result reflects the change in the cell's growth rate. The biomass does not change.
+    """
+    model.objective = "biomass_maintenance"
+    solution_before = model.optimize()
+
+    single_rxn_del = single_reaction_deletion(model, ["GAPD", "PGI", "HEX1"], method="moma", solution=solution_before)
+    print(single_rxn_del)
+
+    with model as model_copy:
+        model_copy.reactions.get_by_id("GAPD").knock_out()  # type: ignore
+        moma_result: cobra.Solution = moma(model_copy, solution_before, linear=False)
+        print("MOMA:", moma_result.objective_value)
+
+
+def fatty_acid_fuel_source(model: cobra.Model):
+    """Validate that naive B cells rely on fatty acids as their main fuel source."""
+    model.objective = "biomass_maintenance"
+    solution_before = model.optimize()
+    with model as model_copy:
+        for group in model_copy.groups:
+            if "fatty acid oxidation" in group.name.lower():
+                for reaction in group.members:
+                    print(reaction.id)
+                    model.reactions.get_by_id(reaction.id).lower_bound = 0  # type: ignore
+                    model.reactions.get_by_id(reaction.id).upper_bound = 0  # type: ignore
+                    print(reaction.lower_bound)
+                    print(reaction.upper_bound)
+        solution_after: cobra.Solution = model_copy.optimize()
+    print(solution_before.objective_value)
+    print(solution_after.objective_value)
+
+
+if __name__ == "__main__":
+    como_paper_model_path = "/Users/satominakamura/Desktop/Dr.Helikar Lab/HealthyNaiveB.xml"  # Path to XML file
+    como_paper_model: cobra.Model = cobra.io.read_sbml_model(como_paper_model_path)
+
+    glucose_restriction(model=como_paper_model)
+    glucose_distribution(model=como_paper_model)
+    lactate_production(model=como_paper_model)
+    citric_acid_cycle_activation(model=como_paper_model)
+    ecar_response_with_glucose_addition(model=como_paper_model)
+    glycolysis_and_oxidative_glucose_pathway_maintenance(model=como_paper_model)
+    fatty_acid_fuel_source(model=como_paper_model)
diff --git a/main/como/utils.py b/main/como/utils.py
index fc8c9739..74716e24 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -1,15 +1,14 @@
 from __future__ import annotations
 
-import asyncio
 import contextlib
-import functools
 import io
 import sys
+import typing
 from collections.abc import Iterator
-from concurrent.futures import ThreadPoolExecutor
-from io import TextIOWrapper
 from pathlib import Path
+from typing import TextIO, TypeVar, cast, overload
 
+import aiofiles
 import numpy.typing as npt
 import pandas as pd
 import scanpy as sc
@@ -24,6 +23,7 @@
 
 from como.data_types import LOG_FORMAT, Algorithm, LogLevel
 
+T = TypeVar("T")
 __all__ = ["split_gene_expression_data", "stringlist_to_list", "suppress_stdout"]
 
 
@@ -35,7 +35,12 @@ def stringlist_to_list(stringlist: str | list[str]) -> list[str]:
     If '[' and ']' are present in the first and last items of the list,
         assume we are using the "old" method of providing context names
 
-    :param stringlist: The "string list" gathered from the command line. Example input: "['mat', 'xml', 'json']"
+    Args:
+        stringlist: The "string list" gathered from the command line. Example input: "['mat', 'xml', 'json']"
+
+    Returns:
+        A list of strings. Example output: ['mat', 'xml', 'json']
+
     """
     if isinstance(stringlist, list):
         return stringlist
@@ -63,13 +68,17 @@ def split_gene_expression_data(
     expression_data: pd.DataFrame,
     recon_algorithm: Algorithm | None = None,
     entrez_as_index: bool = True,
-):
+) -> pd.DataFrame:
     """Split the gene expression data into single-gene and multiple-gene names.
 
-    :param expression_data: The gene expression data to map
-    :param recon_algorithm: The recon algorithm used to generate the gene expression data
-    :param entrez_as_index: Should the 'entrez_gene_id' column be set as the index
-    :return:
+    Args:
+        expression_data: The gene expression data to map
+        recon_algorithm: The recon algorithm used to generate the gene expression data
+        entrez_as_index: Should the 'entrez_gene_id' column be set as the index
+
+    Returns:
+        A pandas DataFrame with the split gene expression data
+
     """
     expression_data.columns = [c.lower() for c in expression_data.columns]
     if recon_algorithm in {Algorithm.IMAT, Algorithm.TINIT}:
@@ -107,11 +116,15 @@ async def _format_determination(
 ) -> pd.DataFrame:
     """Determine the data type of the given input values (i.e., Entrez Gene ID, Gene Symbol, etc.).
 
-    :param biodbnet: The BioDBNet to use for deter
-    :param requested_output: The data type to generate (of type `Output`)
-    :param input_values: The input values to determine
-    :param taxon: The Taxon ID
-    :return: A pandas DataFrame
+    Args:
+        biodbnet: The BioDBNet to use for deter
+        requested_output: The data type to generate (of type `Output`)
+        input_values: The input values to determine
+        taxon: The Taxon ID
+
+    Returns:
+        A pandas DataFrame
+
     """
     requested_output = [requested_output] if isinstance(requested_output, Output) else requested_output
     coercion = (await biodbnet.db_find(values=input_values, output_db=requested_output, taxon=taxon)).drop(columns=["Input Type"])
@@ -119,8 +132,32 @@ async def _format_determination(
     return coercion
 
 
+@overload
+async def _read_file(path: Path | io.StringIO, h5ad_as_df: bool = True, **kwargs) -> pd.DataFrame: ...
+
+
+@overload
+async def _read_file(path: Path | io.StringIO, h5ad_as_df: bool = False, **kwargs) -> pd.DataFrame | sc.AnnData: ...
+
+
+@overload
+async def _read_file(path: None, h5ad_as_df: bool, **kwargs) -> None: ...
+
+
+@overload
+async def _read_file(path: pd.DataFrame, h5ad_as_df: bool, **kwargs) -> pd.DataFrame: ...
+
+
+@overload
+async def _read_file(path: sc.AnnData, h5ad_as_df: bool = False, **kwargs) -> sc.AnnData: ...
+
+
+@overload
+async def _read_file(path: sc.AnnData, h5ad_as_df: bool = True, **kwargs) -> pd.DataFrame: ...
+
+
 async def _read_file(
-    path: Path | io.StringIO | None,
+    path: Path | io.StringIO | pd.DataFrame | sc.AnnData | None,
     h5ad_as_df: bool = True,
     **kwargs,
 ) -> pd.DataFrame | sc.AnnData | None:
@@ -130,27 +167,32 @@ async def _read_file(
     None may be provided to this function so that `asyncio.gather` can safely be used on all sources
         (trna, mrna, scrna, proteomics) without needing to check if the user has provided those sources
 
-    :param path: The path to read from
-    :param kwargs: Additional arguments to pass to pandas.read_csv, pandas.read_excel,
-        or scanpy.read_h5ad, depending on the filepath provided
-    :return: None, or a pandas DataFrame or AnnData
+    Args:
+        path: The path to read from
+        h5ad_as_df: If True and the file is an h5ad, return a pandas DataFrame of the .X matrix instead of an AnnData object
+        kwargs: Additional arguments to pass to pandas.read_csv, pandas.read_excel, or scanpy.read_h5ad, depending on the filepath provided
+
+    Returns:
+        None, or a pandas DataFrame or AnnData
+
     """
-    if not path:
+    if isinstance(path, pd.DataFrame):
+        return path
+    elif isinstance(path, sc.AnnData):
+        return path.to_df().T if h5ad_as_df else path
+    elif isinstance(path, io.StringIO):
+        return pd.read_csv(path, **kwargs)
+    elif not path:
         return None
 
     if isinstance(path, Path) and not path.exists():
         _log_and_raise_error(f"File {path} does not exist", error=FileNotFoundError, level=LogLevel.CRITICAL)
 
-    # StringIO is used if a CSV file is read using open() directly
-    if isinstance(path, io.StringIO):
-        return pd.read_csv(path, **kwargs)
-
     match path.suffix:
         case ".csv" | ".tsv" | ".txt":
-            if "sep" not in kwargs:
-                kwargs.setdefault("sep", "," if path.suffix == ".csv" else "\t")
-            with path.open("r") as i_stream:
-                content = i_stream.read()
+            kwargs.setdefault("sep", "," if path.suffix == ".csv" else "\t")  # set sep if not defined
+            async with aiofiles.open(path) as i_stream:
+                content = await i_stream.read()
                 return pd.read_csv(io.StringIO(content), **kwargs)
         case ".xlsx" | ".xls":
             return pd.read_excel(path, **kwargs)
@@ -168,7 +210,6 @@ async def _read_file(
                 error=ValueError,
                 level=LogLevel.CRITICAL,
             )
-            return None
 
 
 async def get_missing_gene_data(values: list[str] | pd.DataFrame, taxon_id: int | str | Taxon) -> pd.DataFrame:
@@ -196,9 +237,27 @@ async def get_missing_gene_data(values: list[str] | pd.DataFrame, taxon_id: int
             raise ValueError("Unable to find 'gene_symbol', 'entrez_gene_id', or 'ensembl_gene_id' in the input matrix.")
 
 
-def _listify(value):
-    """Convert items into a list."""
-    return [value] if not isinstance(value, list) else value
+@overload
+def _listify(value: list[T]) -> list[T]: ...
+
+
+@overload
+def _listify(value: T) -> list[T]: ...
+
+
+def _listify(value: T | list[T]) -> list[T]:
+    """Convert items into a list.
+
+    Args:
+        value: The value to convert to a list (if it isn't already)
+
+    Returns:
+        A list of the provided value
+
+    """
+    if isinstance(value, list):
+        return cast(list[T], value)  # does not actually do anything; signifies to type checker that return value is of type list[T]
+    return [value]
 
 
 def _num_rows(item: pd.DataFrame | npt.NDArray) -> int:
@@ -215,7 +274,7 @@ def return_placeholder_data() -> pd.DataFrame:
 
 def _set_up_logging(
     level: LogLevel | str,
-    location: str | TextIOWrapper,
+    location: str | TextIO,
     formatting: str = LOG_FORMAT,
 ):
     if isinstance(level, str):
@@ -230,7 +289,7 @@ def _log_and_raise_error(
     *,
     error: type[BaseException],
     level: LogLevel,
-) -> None:
+) -> typing.NoReturn:
     caller = logger.opt(depth=1)
     match level:
         case LogLevel.ERROR:
diff --git a/main/data/boundary_rxns/naiveB_boundary_rxns.csv b/main/data/boundary_rxns/naiveB_boundary_rxns.csv
index 0b08556e..7130c9be 100644
--- a/main/data/boundary_rxns/naiveB_boundary_rxns.csv
+++ b/main/data/boundary_rxns/naiveB_boundary_rxns.csv
@@ -1,4 +1,4 @@
-Boundary,Abbreviation,Compartment,Minimum Reaction Rate,Maximum Reaction Rate
+Reaction,Abbreviation,Compartment,Minimum Reaction Rate,Maximum Reaction Rate
 Exchange,glc_D,Extracellular,-100,1000
 Exchange,fe2,Extracellular,-1000,1000
 Exchange,gal,Extracellular,0,1000
diff --git a/pyproject.toml b/pyproject.toml
index f59f4ed3..67c6b4f8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,7 +5,7 @@ build-backend = "hatchling.build"
 [project]
 name = "COMO"
 dynamic = ["version"]
-requires-python = ">=3.10,<3.13"
+requires-python = ">=3.10,<3.14"
 dependencies = [
     "aiofiles>=24.1.0",
     "aioftp>=0.23.1",
diff --git a/tests/unit/test_data_types.py b/tests/unit/test_data_types.py
index 45939ba8..3b24f9a2 100644
--- a/tests/unit/test_data_types.py
+++ b/tests/unit/test_data_types.py
@@ -3,7 +3,7 @@
 
 def test_source_types():
     """Validate that source types always go in the order of 'trna', 'mrna', 'scrna', 'protemics'."""
-    expected_order = ["trna", "mrna", "scrna", "proteomics"]
+    expected_order = ["TRNA", "MRNA", "SCRNA", "PROTEOMICS"]
     for expected, source in zip(expected_order, SourceTypes, strict=True):
         expected: str
         source: SourceTypes
diff --git a/uv.lock b/uv.lock
index 1a227653..62d06f68 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,6 @@
 version = 1
-requires-python = ">=3.10, <3.13"
+revision = 3
+requires-python = ">=3.10, <3.14"
 resolution-markers = [
     "python_full_version >= '3.12'",
     "python_full_version == '3.11.*'",
@@ -10,130 +11,156 @@ resolution-markers = [
 name = "aiofiles"
 version = "24.1.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247 }
+sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247, upload-time = "2024-06-24T11:02:03.584Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5", size = 15896, upload-time = "2024-06-24T11:02:01.529Z" },
 ]
 
 [[package]]
 name = "aioftp"
-version = "0.23.1"
+version = "0.27.2"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/b5/be/8788b5ef25efb86261e4e69b5b21de472c6a5d39d06842dc5c5d19772506/aioftp-0.23.1.tar.gz", hash = "sha256:b80eadd8ca95d2bbbcfabe7de15cbe01ac117b2e7467717374de467bad93570b", size = 62481 }
+dependencies = [
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/cc/a3/d1af4b69c09dc9955fdb329de5a9ea73f6a3ba50ac4861882453254ffa99/aioftp-0.27.2.tar.gz", hash = "sha256:7c048c3220081796c59832a6fd9fd8fad97e3cec12a50be3abccf2dcbbeb261a", size = 70248, upload-time = "2025-09-24T21:51:31.297Z" }
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 ]
 
 [[package]]
 name = "anndata"
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 source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
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+]
 dependencies = [
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+    { name = "array-api-compat", marker = "python_full_version < '3.11'" },
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-    { name = "natsort" },
-    { name = "numpy" },
-    { name = "packaging" },
-    { name = "pandas" },
-    { name = "scipy" },
+    { name = "h5py", marker = "python_full_version < '3.11'" },
+    { name = "natsort", marker = "python_full_version < '3.11'" },
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 dependencies = [
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@@ -356,7 +413,8 @@ dependencies = [
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 [[package]]
 name = "como"
-version = "1.11.1"
 source = { editable = "." }
 dependencies = [
+    { name = "aiofiles" },
     { name = "aioftp" },
     { name = "cobamp" },
     { name = "cobra" },
     { name = "fast-bioservices" },
     { name = "kaleido" },
     { name = "loguru" },
-    { name = "numpy" },
     { name = "openpyxl" },
     { name = "pandas" },
+    { name = "plotly" },
+    { name = "pydantic" },
+    { name = "pydantic-settings" },
     { name = "scanpy" },
     { name = "scikit-learn" },
-    { name = "scipy" },
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+    { name = "scipy", version = "1.16.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "seaborn" },
     { name = "setuptools" },
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-    { name = "commitlint" },
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     { name = "pytest" },
     { name = "pytest-asyncio" },
@@ -459,22 +484,17 @@ gurobi = [
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 ]
 interactive = [
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+    { name = "ipython", version = "9.6.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "jupyterlab" },
 ]
 
-[package.dev-dependencies]
-dev = [
-    { name = "como" },
-]
-
 [package.metadata]
 requires-dist = [
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     { name = "openpyxl", specifier = ">=3.1.5" },
     { name = "pandas", specifier = ">=1.3.5" },
+    { name = "plotly", specifier = ">=5.24.1" },
+    { name = "pydantic", specifier = ">=2.10.3" },
+    { name = "pydantic-settings" },
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     { name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.24.0" },
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-    { name = "scanpy", specifier = ">=1.10.4" },
+    { name = "scanpy", specifier = ">=1.11.4" },
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     { name = "troppo", git = "https://github.com/JoshLoecker/troppo?rev=master" },
 ]
+provides-extras = ["dev", "gurobi", "interactive"]
 
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From dfadcea577bfdfce5ecf00df42c94adea89e7de6 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 6 Oct 2025 14:37:39 -0500
Subject: [PATCH 332/345] chore: move development dependencies to
 `dependency-groups.dev`

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 pyproject.toml |  22 ++--
 uv.lock        | 278 +++----------------------------------------------
 2 files changed, 26 insertions(+), 274 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 739f2d61..8e55f619 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -27,22 +27,13 @@ dependencies = [
     "troppo@git+https://github.com/JoshLoecker/troppo@master",
 ]
 
+# User-facing features go into the optional-dependencies
 [project.optional-dependencies]
-gurobi = [ "gurobipy<14" ]
-interactive =   [
+gurobi = ["gurobipy<14"]
+interactive = [
     "ipython>=8.0.0",
     "jupyterlab>=4.3.2"
 ]
-dev = [
-    "commitlint>=1.3.0",
-    "pytest-asyncio>=0.24.0",
-    "pytest>=8.3.3",
-    "ruff>=0.8.0",
-    "hypothesis>=6.122.1",
-    "pytest-cov>=6.0.0",
-    "commitizen>=4.1.0",
-]
-
 
 [tool.hatch.version]
 path = "main/como/__init__.py"
@@ -54,7 +45,7 @@ packages = ["main/como"]
 allow-direct-references = true
 
 [tool.pytest.ini_options]
-pythonpath = [ "main/src" ]
+pythonpath = ["main/src"]
 
 [tool.commitizen]
 name = "cz_conventional_commits"
@@ -69,8 +60,13 @@ cobra = { git = "https://github.com/ripaul/cobrapy", rev = "hopsy-backend" }
 
 [dependency-groups]
 dev = [
+    "commitizen>=4.8.3",
+    "commitlint>=1.3.1",
     "como",
     "pandas-stubs>=2.3.2.250827",
     "pre-commit>=4.2.0",
+    "pytest-asyncio>=1.1.0",
+    "pytest>=8.4.1",
+    "ruff>=0.12.11",
     "scipy-stubs>=1.16.1.1",
 ]
diff --git a/uv.lock b/uv.lock
index e2c23f0c..937aa52a 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.11, <3.14"
 resolution-markers = [
     "python_full_version >= '3.12'",
@@ -149,27 +149,6 @@ wheels = [
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-[[package]]
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-version = "0.22.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "h5netcdf" },
-    { name = "matplotlib" },
-    { name = "numpy" },
-    { name = "packaging" },
-    { name = "pandas" },
-    { name = "scipy" },
-    { name = "setuptools" },
-    { name = "typing-extensions" },
-    { name = "xarray" },
-    { name = "xarray-einstats" },
-]
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-]
-
 [[package]]
 name = "asttokens"
 version = "3.0.0"
@@ -457,8 +436,6 @@ dependencies = [
     { name = "cobamp" },
     { name = "cobra" },
     { name = "fast-bioservices" },
-    { name = "gurobipy" },
-    { name = "hopsy" },
     { name = "kaleido" },
     { name = "loguru" },
     { name = "numpy" },
@@ -474,14 +451,8 @@ dependencies = [
 ]
 
 [package.optional-dependencies]
-dev = [
-    { name = "commitizen" },
-    { name = "commitlint" },
-    { name = "hypothesis" },
-    { name = "pytest" },
-    { name = "pytest-asyncio" },
-    { name = "pytest-cov" },
-    { name = "ruff" },
+gurobi = [
+    { name = "gurobipy" },
 ]
 interactive = [
     { name = "ipython" },
@@ -490,9 +461,14 @@ interactive = [
 
 [package.dev-dependencies]
 dev = [
+    { name = "commitizen" },
+    { name = "commitlint" },
     { name = "como" },
     { name = "pandas-stubs" },
     { name = "pre-commit" },
+    { name = "pytest" },
+    { name = "pytest-asyncio" },
+    { name = "ruff" },
     { name = "scipy-stubs" },
 ]
 
@@ -502,23 +478,15 @@ requires-dist = [
     { name = "anndata", specifier = ">=0.12.0" },
     { name = "cobamp", git = "https://github.com/JoshLoecker/cobamp?rev=master" },
     { name = "cobra", git = "https://github.com/ripaul/cobrapy?rev=hopsy-backend" },
-    { name = "commitizen", marker = "extra == 'dev'", specifier = ">=4.1.0" },
-    { name = "commitlint", marker = "extra == 'dev'", specifier = ">=1.3.0" },
     { name = "fast-bioservices", specifier = ">=0.3.9" },
-    { name = "gurobipy", specifier = "<14" },
-    { name = "hopsy", specifier = ">=1.6.1" },
-    { name = "hypothesis", marker = "extra == 'dev'", specifier = ">=6.122.1" },
+    { name = "gurobipy", marker = "extra == 'gurobi'", specifier = "<14" },
     { name = "ipython", marker = "extra == 'interactive'", specifier = ">=8.0.0" },
     { name = "jupyterlab", marker = "extra == 'interactive'", specifier = ">=4.3.2" },
-    { name = "kaleido", specifier = ">=0.2.1" },
+    { name = "kaleido", specifier = ">=1.0.0" },
     { name = "loguru", specifier = ">=0.7.2" },
-    { name = "numpy", specifier = ">2.0" },
+    { name = "numpy", specifier = ">=2" },
     { name = "openpyxl", specifier = ">=3.1.5" },
     { name = "pandas", specifier = ">=1.3.5" },
-    { name = "pytest", marker = "extra == 'dev'", specifier = ">=8.3.3" },
-    { name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.24.0" },
-    { name = "pytest-cov", marker = "extra == 'dev'", specifier = ">=6.0.0" },
-    { name = "ruff", marker = "extra == 'dev'", specifier = ">=0.8.0" },
     { name = "scanpy", specifier = ">=1.10.4" },
     { name = "scikit-learn", specifier = ">=1.5.2" },
     { name = "scipy", specifier = ">=1.13.0" },
@@ -528,13 +496,18 @@ requires-dist = [
     { name = "statsmodels", marker = "python_full_version >= '3.12'", specifier = ">=0.14.0" },
     { name = "troppo", git = "https://github.com/JoshLoecker/troppo?rev=master" },
 ]
-provides-extras = ["dev", "interactive"]
+provides-extras = ["gurobi", "interactive"]
 
 [package.metadata.requires-dev]
 dev = [
+    { name = "commitizen", specifier = ">=4.8.3" },
+    { name = "commitlint", specifier = ">=1.3.1" },
     { name = "como", editable = "." },
     { name = "pandas-stubs", specifier = ">=2.3.2.250827" },
     { name = "pre-commit", specifier = ">=4.2.0" },
+    { name = "pytest", specifier = ">=8.4.1" },
+    { name = "pytest-asyncio", specifier = ">=1.1.0" },
+    { name = "ruff", specifier = ">=0.12.11" },
     { name = "scipy-stubs", specifier = ">=1.16.1.1" },
 ]
 
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-    { url = "https://files.pythonhosted.org/packages/6e/c2/61d3e0f47e2b74ef40a68b9e6ad5984f6241a942f7cd3bbfbdbd03861ea9/tomli-2.2.1-py3-none-any.whl", hash = "sha256:cb55c73c5f4408779d0cf3eef9f762b9c9f147a77de7b258bef0a5628adc85cc", size = 14257, upload-time = "2024-11-27T22:38:35.385Z" },
-]
-
 [[package]]
 name = "tomlkit"
 version = "0.13.3"
@@ -3569,34 +3353,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e1/07/c6fe3ad3e685340704d314d765b7912993bcb8dc198f0e7a89382d37974b/win32_setctime-1.2.0-py3-none-any.whl", hash = "sha256:95d644c4e708aba81dc3704a116d8cbc974d70b3bdb8be1d150e36be6e9d1390", size = 4083, upload-time = "2024-12-07T15:28:26.465Z" },
 ]
 
-[[package]]
-name = "xarray"
-version = "2025.8.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "numpy" },
-    { name = "packaging" },
-    { name = "pandas" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/d2/55/18055bc943029d25fb8f260b7e3b1485c30646ccf503a5e4a744d31a3b78/xarray-2025.8.0.tar.gz", hash = "sha256:323d4169ce72d4ef849de2b0bd122f9cd2905b82c7558169930dc16070982bab", size = 3034425, upload-time = "2025-08-14T16:52:13.872Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/e6/c8/0f8db9d9478de8d70cbcae2056588401e26168e269d6d9919bf2ecb01f78/xarray-2025.8.0-py3-none-any.whl", hash = "sha256:1c454f32b38c93df68e450238c9473fe21248b8572d42ddd58c5170bb30934ee", size = 1342279, upload-time = "2025-08-14T16:52:10.956Z" },
-]
-
-[[package]]
-name = "xarray-einstats"
-version = "0.9.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "numpy" },
-    { name = "scipy" },
-    { name = "xarray" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/f1/10/ef474494a7f2102ec4c02352c723fa282c6237b600565eb82ee354291211/xarray_einstats-0.9.1.tar.gz", hash = "sha256:39b373deed43592c41d3fbf8863af62e19e01c1ae553ae5ff059a8df78d995c6", size = 33327, upload-time = "2025-06-18T15:53:28.499Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/17/8b/ed2f0f49385c3d7739cd4699954add26e8f09a372a0c3f04f2bde32fcea2/xarray_einstats-0.9.1-py3-none-any.whl", hash = "sha256:777339524e85d066f2ef9ed1e3a3fb63aead4c1065fd1406f30dfa4de58ce063", size = 39043, upload-time = "2025-06-18T15:53:24.088Z" },
-]
-
 [[package]]
 name = "xlrd"
 version = "2.0.2"

From c8292ce0b8965a789842011b5ae25de54a142f26 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 6 Oct 2025 15:53:28 -0500
Subject: [PATCH 333/345] chore: merge imports

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/utils.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/main/como/utils.py b/main/como/utils.py
index 7a5be705..1d0efb3e 100644
--- a/main/como/utils.py
+++ b/main/como/utils.py
@@ -6,8 +6,7 @@
 from collections.abc import Iterator
 from io import TextIOWrapper
 from pathlib import Path
-from typing import TypeVar, cast, overload
-from typing import Literal
+from typing import Literal, TypeVar, cast, overload
 
 import aiofiles
 import numpy.typing as npt

From 894be61005b0e46840a2e7df19ee4982a382c54f Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 6 Oct 2025 15:54:14 -0500
Subject: [PATCH 334/345] chore: remove unused imports

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 478e1ed0..590fddfa 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -1,16 +1,14 @@
 from __future__ import annotations
 
 import asyncio
-import functools
-import json
 import json
 import re
 import sys
 from dataclasses import asdict, dataclass, field
-from io import StringIO, TextIOWrapper
+from io import StringIO
 from itertools import chain
 from pathlib import Path
-from typing import Final, Literal, cast, TextIO
+from typing import Final, Literal, TextIO
 
 import aiofiles
 import numpy as np

From cd16b5fa811f0054635340e37c79ecbbb363927f Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 6 Oct 2025 15:54:59 -0500
Subject: [PATCH 335/345] chore: create explicit variable for `re.search()`
 call

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 590fddfa..76c97163 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -160,7 +160,10 @@ async def _read_text(path: Path | None, *, default: str, lower: bool = False) ->
 
 
 def _sample_name_from_filepath(file: Path) -> str:
-    return re.search(r".+_S\d+R\d+", file.stem).group()
+    search = re.search(r".+_S\d+R\d+(r\d+)?", file.stem)
+    if not search:
+        raise ValueError(f"Could not extract sample name from filepath '{file}'")
+    return search.group()
 
 
 def _require_one(paths: list[Path], kind: Literal["layout", "strand", "preparation", "fragment"], label: str) -> Path | None:

From c07457e7db7b667bbd394d9860f2d0250a6142b7 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 6 Oct 2025 15:55:13 -0500
Subject: [PATCH 336/345] chore: use `cast` for better type hints

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 76c97163..7cfe136e 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -8,7 +8,7 @@
 from io import StringIO
 from itertools import chain
 from pathlib import Path
-from typing import Final, Literal, TextIO
+from typing import Final, Literal, TextIO, cast
 
 import aiofiles
 import numpy as np
@@ -343,7 +343,10 @@ async def _write_counts_matrix(
         A pandas DataFrame representing the final counts matrix.
     """
     study_metrics = _organize_gene_counts_files(data_dir=como_context_dir)
-    counts: list[pd.DataFrame] = await asyncio.gather(*[_create_sample_counts_matrix(metric) for metric in study_metrics])    counts: list[pd.DataFrame] = await asyncio.gather(*[_create_sample_counts_matrix(metric) for metric in study_metrics])
+    counts: list[pd.DataFrame] = cast(
+        typ=list[pd.DataFrame],
+        val=await asyncio.gather(*[_create_sample_counts_matrix(metric) for metric in study_metrics]),
+    )
 
     final_matrix = pd.DataFrame()
     for count in counts:

From e6e7076454743edb4d242cdb0239138318a01e66 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 6 Oct 2025 15:55:21 -0500
Subject: [PATCH 337/345] chore: expand docstring

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_preprocess.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/main/como/rnaseq_preprocess.py b/main/como/rnaseq_preprocess.py
index 7cfe136e..9de60976 100644
--- a/main/como/rnaseq_preprocess.py
+++ b/main/como/rnaseq_preprocess.py
@@ -808,15 +808,13 @@ async def rnaseq_preprocess(
         output_mrna_metadata_filepath: Path to the output mRNA config file (if in "create" mode)
         output_trna_count_matrix_filepath: The path to write total RNA count matrices
         output_mrna_count_matrix_filepath: The path to write messenger RNA count matrices
-        como_context_dir: If in "create" mode, the input path(s) to the COMO_input directory of the current context i.e., the directory containing "fragmentSizes", "geneCounts", "insertSizeMetrics", etc. directories
+        como_context_dir: If in "create" mode, the input path(s) to the COMO_input directory of the current context
+            i.e., the directory containing "fragmentSizes", "geneCounts", "insertSizeMetrics", etc. directories
         input_matrix_filepath: If in "provide" mode, the path(s) to the count matrices to be processed~
         cache: Should HTTP requests be cached
         log_level: The logging level
         log_location: The logging location
         create_gene_info_only: If True, only create the gene info file and skip general preprocessing steps
-    
-    Returns:
-        None
 
     """
     _set_up_logging(level=log_level, location=log_location)

From 3ec6b1d3e9ca3c3fb5f07b2c6ee90fd227f2236a Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 6 Oct 2025 15:55:30 -0500
Subject: [PATCH 338/345] chore: expand docstring

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/sanity_checks/fast_leak_test.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/main/como/sanity_checks/fast_leak_test.py b/main/como/sanity_checks/fast_leak_test.py
index 0ce92c1a..d1e0fdc5 100644
--- a/main/como/sanity_checks/fast_leak_test.py
+++ b/main/como/sanity_checks/fast_leak_test.py
@@ -40,6 +40,11 @@ def fast_leak_test(
         demand_test: If 'TRUE', demand reactions for all metabolites in the model are created
         tolerance: Tolerance level for determining significant flux
 
+    Returns:
+        leak_mets: List of exchange reactions for leaking metabolites
+        model_closed: Model structure that has been tested for leaks
+        flux_ex_v: Flux vector for computed exchange reactions in the closed model
+
     """
     model_closed = model.copy()
 

From 72945343c3397bcbeaca37078f8bab10ace7920d Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 6 Oct 2025 15:56:05 -0500
Subject: [PATCH 339/345] chore: ignore inheriting from `(str, enum.Enum)`
 instead of `enum.StrEnum`

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/data_types.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/main/como/data_types.py b/main/como/data_types.py
index 977a5a20..d05f8db5 100644
--- a/main/como/data_types.py
+++ b/main/como/data_types.py
@@ -14,7 +14,7 @@
 LOG_FORMAT = "<green>{time:YYYY-MM-DD HH:mm:ss}</> | <level>{level:<8}</> | <cyan>{name}</>:<cyan>{line}</> - <level>{message}</>"
 
 
-class AdjustmentMethod(str, Enum):
+class AdjustmentMethod(str, Enum):  # noqa: UP042
     """Adjustment method for expression requirement based on differences in number of provided data source types."""
 
     PROGRESSIVE = "PROGRESSIVE"
@@ -23,14 +23,14 @@ class AdjustmentMethod(str, Enum):
     CUSTOM = "CUSTOM"
 
 
-class Algorithm(str, Enum):
+class Algorithm(str, Enum):  # noqa: UP042
     GIMME = "GIMME"
     FASTCORE = "FASTCORE"
     IMAT = "IMAT"
     TINIT = "TINIT"
 
 
-class FilteringTechnique(str, Enum):
+class FilteringTechnique(str, Enum):  # noqa: UP042
     """RNA sequencing filtering capabilities."""
 
     CPM = "CPM"
@@ -39,7 +39,7 @@ class FilteringTechnique(str, Enum):
     UMI = "UMI"
 
 
-class GeneIdentifier(str, Enum):
+class GeneIdentifier(str, Enum):  # noqa: UP042
     ENSEMBL_GENE_ID = "ENSEMBL_GENE_ID"
     ENTREZ_GENE_ID = "ENTREZ_GENE_ID"
     GENE_SYMBOL = "GENE_SYMBOL"
@@ -56,13 +56,13 @@ class LogLevel(int, Enum):
     NONE = 100
 
 
-class RNAType(str, Enum):
+class RNAType(str, Enum):  # noqa: UP042
     TRNA = "TOTAL"
     MRNA = "MRNA"
     SCRNA = "SCRNA"
 
 
-class Solver(str, Enum):
+class Solver(str, Enum):  # noqa: UP042
     """Solver used to seed context specific model."""
 
     GLPK = "GLPK"
@@ -71,7 +71,7 @@ class Solver(str, Enum):
     GLPK_EXACT = "GLPK_EXACT"
 
 
-class SourceTypes(str, Enum):
+class SourceTypes(str, Enum):  # noqa: UP042
     TRNA = "TRNA"
     MRNA = "MRNA"
     SCRNA = "SCRNA"

From f0ec80b277c9d07fd33e6523a26b0609e544769d Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 6 Oct 2025 15:56:42 -0500
Subject: [PATCH 340/345] chore: expand docstring

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/data_types.py   | 28 ++++++++++++++++++++++++----
 main/como/merge_xomics.py | 30 ++++++++++++++++++++++++------
 main/como/rnaseq_gen.py   | 14 ++++++--------
 3 files changed, 54 insertions(+), 18 deletions(-)

diff --git a/main/como/data_types.py b/main/como/data_types.py
index d05f8db5..7e3658b3 100644
--- a/main/como/data_types.py
+++ b/main/como/data_types.py
@@ -139,12 +139,28 @@ class CobraCompartments:
 
     @classmethod
     def get_shorthand(cls, longhand: str) -> str | None:
-        """Get the short-hand compartment name from the long-hand name."""
+        """Get the short-hand compartment name from the long-hand name.
+
+        Args:
+            longhand: The long-hand compartment name (e.g., 'extracellular').
+
+        Returns:
+            The short-hand compartment name if found, None otherwise.
+
+        """
         return cls._REVERSE_LOOKUP.get(longhand.lower(), None)
 
     @classmethod
     def get_longhand(cls, shorthand: str) -> str | None:
-        """Get the long-hand compartment name from the short-hand name."""
+        """Get the long-hand compartment name from the short-hand name.
+
+        Args:
+            shorthand: The short-hand compartment name (e.g., 'e' for extracellular).
+
+        Returns:
+            The long-hand compartment name if found, None otherwise.
+
+        """
         longhand = cls.SHORTHAND.get(shorthand.lower(), None)
         return longhand[0] if longhand else None
 
@@ -192,8 +208,12 @@ class _BaseDataType:
     def __getitem__(self, value: str):
         """Access matrices using square bracket notation (e.g., `input_matrices['total_rna']`).
 
-        :param value: The name of the matrix to get ('trna', 'mrna', 'scrna', 'proteomics')
-        :returns: The DataFrame if it exists, None otherwise.
+        Args:
+            value: The name of the matrix to get ('trna', 'mrna', 'scrna', 'proteomics')
+
+        Returns:
+            The DataFrame if it exists, None otherwise.
+
         """
         self._validate_attribute(value)
         return getattr(self, value)
diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index c017bf4a..37c93d58 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -50,8 +50,18 @@ class _HighExpressionHeaderNames:
 
 
 # TODO: If function is no longer needed, remove?
-async def _load_rnaseq_tests(filename, context_name, prep_method: RNAType) -> tuple[str, pd.DataFrame]:
-    """Load rnaseq results."""
+def _load_rnaseq_tests(filename, context_name, prep_method: RNAType) -> tuple[str, pd.DataFrame]:
+    """Load rnaseq results.
+    
+    Args:
+        filename: Filename of the RNA-seq data
+        context_name: Name of the context
+        prep_method: Preparation method used for RNA-seq data
+        
+    Returns:
+        A tuple containing the context name and the RNA-seq data as a pandas DataFrame
+
+    """
     logger.debug(f"Loading data for context '{context_name}' using preparation method '{prep_method.value}'")
     config = Config()
 
@@ -90,8 +100,11 @@ def load_dummy_dict():
 def _merge_logical_table(df: pd.DataFrame):
     """Merge rows of Logical Table belonging to the same entrez_gene_id.
 
-    :param df: pandas dataframe of logical table
-    :return: pandas dataframe of merged table
+    Args:
+        df: A logical/boolean table
+    
+    Returns:
+        pandas dataframe of merged table
     """
     # step 1: get all plural ENTREZ_GENE_IDs in the input table, extract unique IDs
     df.dropna(subset=["entrez_gene_id"], inplace=True)
@@ -180,8 +193,13 @@ async def _get_transcriptmoic_details(merged_df: pd.DataFrame, taxon_id: int) ->
     The resulting dataframe will have its columns created in the order listed above
     It will return a pandas dataframe with this information
 
-    :param merged_df: A dataframe containing all active transcriptomic and proteomic genes
-    :return: A dataframe with the above-listed columns
+    Args:
+        merged_df: A dataframe containing all active transcriptomic and proteomic genes
+        taxon_id: The NCBI taxonomy ID of the organism
+    
+    Returns:
+        A dataframe with the above-listed columns
+
     """
     # If _ExpressedHeaderNames.PROTEOMICS.value is in the dataframe, lower the required expression by 1
     # We are only trying to get details for transcriptomic data
diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 57006f49..fa9a4282 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -360,7 +360,7 @@ def _zfpkm_calculation(
         column: A Pandas Series containing FPKM values for a single sample.
         peak_parameters: Parameters for peak identification in zFPKM calculation.
         bandwidth: The bandwidth for kernel density estimation in zFPKM calculation.
-      epsilon: A small value to add to FPKM values to prevent log2-divide-by-0 errors
+        epsilon: A small value to add to FPKM values to prevent log2-divide-by-0 errors
 
     Returns:
         A named tuple containing:
@@ -680,7 +680,7 @@ def zfpkm_filter(
         force_zfpkm_plot: Whether to force plotting of zFPKM results even if there are many samples.
         peak_parameters: Parameters for peak identification in zFPKM calculation.
         bandwidth: The bandwidth for kernel density estimation in zFPKM calculation.
-        output_png_filepath: Optional filepath to save the zFPKM plot.
+        output_png_dirpath: Optional directory path to save the zFPKM plot.
 
     Returns:
         A dictionary of filtered study metrics.
@@ -749,7 +749,7 @@ def filter_counts(
         force_zfpkm_plot: Whether to force plotting of zFPKM results even if there are many samples.
         peak_parameters: Parameters for peak identification in zFPKM calculation.
         bandwidth: The bandwidth for kernel density estimation in zFPKM calculation.
-        output_png_filepath: Optional filepath to save the zFPKM plot.
+        output_zfpkm_plot_dirpath: Optional directory path to save the zFPKM plot.
 
     Returns:
         A dictionary of filtered study metrics.
@@ -944,7 +944,8 @@ async def rnaseq_gen(  # noqa: C901
         input_metadata_filepath_or_df: The filepath or dataframe containing metadata information
         replicate_ratio: The percentage of replicates that a gene must appear in for a gene to be marked as "active" in a batch/study
         batch_ratio: The percentage of batches that a gene must appear in for a gene to be marked as 'active"
-        high_replicate_ratio: The percentage of replicates that a gene must appear in for a gene to be marked "highly confident" in its expression in a batch/study
+        high_replicate_ratio: The percentage of replicates that a gene must appear in for a gene to be
+            marked "highly confident" in its expression in a batch/study
         high_batch_ratio: The percentage of batches that a gene must appear in for a gene to be marked "highly confident" in its expression
         technique: The filtering technique to use
         zfpkm_peak_height: The height of the zFPKM peak
@@ -955,10 +956,7 @@ async def rnaseq_gen(  # noqa: C901
         log_level: The level of logging to output
         log_location: The location to write logs to
         output_zfpkm_plot_dirpath: Optional filepath to save zFPKM plots
-    
-    Returns:
-        None
-    :return: None
+
     """
     _set_up_logging(level=log_level, location=log_location)
 

From 51bd8db56710ecfda3c443fda5eca55194c0e7e9 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 6 Oct 2025 15:57:07 -0500
Subject: [PATCH 341/345] fix: initial fix for modifying sequence while
 iterating

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/merge_xomics.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/main/como/merge_xomics.py b/main/como/merge_xomics.py
index 37c93d58..5055d6f7 100644
--- a/main/como/merge_xomics.py
+++ b/main/como/merge_xomics.py
@@ -144,12 +144,15 @@ def _merge_logical_table(df: pd.DataFrame):
         set1 = set(multiple_entrez_ids[i].split("//"))
         multi_entrez_index.remove(i)
 
-        for j in multi_entrez_index:
+        multi_entrez_index_to_remove: set[int] = set()
+        for j in range(len(multi_entrez_index)):
             set2 = set(multiple_entrez_ids[j].split("//"))
             intersect = set1.intersection(set2)
             if bool(intersect):
                 set1 = set1.union(set2)
-                multi_entrez_index.remove(j)
+                multi_entrez_index_to_remove.add(j)
+        for index in multi_entrez_index_to_remove:
+            multi_entrez_index.pop(index)
 
         sortlist = list(set1)
         sortlist.sort(key=int)

From ce193d1cf9f93d6943727a058695deb83329cf8d Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 6 Oct 2025 15:57:20 -0500
Subject: [PATCH 342/345] chore: use `|` for type union

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/proteomics_preprocessing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/main/como/proteomics_preprocessing.py b/main/como/proteomics_preprocessing.py
index 39d4c85c..510424be 100644
--- a/main/como/proteomics_preprocessing.py
+++ b/main/como/proteomics_preprocessing.py
@@ -156,7 +156,7 @@ def protein_transform_main(
     output_z_score_matrix_filepath: Path,
 ) -> None:
     """Transform protein abundance data."""
-    abundance_df: pd.DataFrame = pd.read_csv(abundance_df) if isinstance(abundance_df, (str, Path)) else abundance_df.fillna(0)
+    abundance_df: pd.DataFrame = pd.read_csv(abundance_df) if isinstance(abundance_df, str | Path) else abundance_df.fillna(0)
     abundance_df = abundance_df[np.isfinite(abundance_df).all(axis=1)]  # Remove +/- infinity values
     z_transform: ZResult = z_score_calc(abundance_df, min_thresh=0)
 

From af5069d5a4a671b5b4d40a1c1adc322847b167c3 Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 6 Oct 2025 15:57:43 -0500
Subject: [PATCH 343/345] chore: ruff formatting

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index fa9a4282..87c34d67 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -418,10 +418,7 @@ def zfpkm_transform(
             - A DataFrame of zFPKM values with the same shape as the input fpkm_df.
     """
     if update_every_percent > 1:
-        logger.warning(
-            f"update_every_percent should be a decimal value between 0 and 1; got: {update_every_percent} - "
-            f"will convert to percentage"
-        )
+        logger.warning(f"update_every_percent should be a decimal value between 0 and 1; got: {update_every_percent} - will convert to percentage")
         update_every_percent /= 100
 
     total_samples = _num_columns(fpkm_df)
@@ -507,9 +504,8 @@ def zfpkm_plot(results: dict[str, _ZFPKMResult], *, output_png_dirpath: Path, pl
     mega_df.columns = pd.Series(data=["sample_name", "log2fpkm", "fpkm_density", "fitted_density_scaled"])
     mega_df = mega_df.melt(id_vars=["log2fpkm", "sample_name"], var_name="source", value_name="density")
 
-    fig: plt.Figure
     axes: list[plt.Axes]
-    fig, axes = plt.subplots(nrows=len(results), ncols=1, figsize=(8, 4 * len(results)))
+    _, axes = plt.subplots(nrows=len(results), ncols=1, figsize=(8, 4 * len(results)))
     if len(results) == 1:
         axes = [axes]
 

From 1283fdb424cd50be8d12c05c10c3dba11860306e Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 6 Oct 2025 15:58:10 -0500
Subject: [PATCH 344/345] fix: use `[i]` to assign values instead of
 `.append()`

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 main/como/rnaseq_gen.py | 16 +++-------------
 1 file changed, 3 insertions(+), 13 deletions(-)

diff --git a/main/como/rnaseq_gen.py b/main/como/rnaseq_gen.py
index 87c34d67..c0c74225 100644
--- a/main/como/rnaseq_gen.py
+++ b/main/como/rnaseq_gen.py
@@ -479,7 +479,7 @@ def zfpkm_plot(results: dict[str, _ZFPKMResult], *, output_png_dirpath: Path, pl
 
     """
     to_concat: list[pd.DataFrame] = [None] * len(results)  # type: ignore  # ignoring because None is not of type pd.DataFrame
-    for name, result in results.items():
+    for i, name, result in enumerate(results.items()):
         stddev: float = float(result.std_dev)
         x: npt.NDArray[float] = result.density.x.flatten()
         y: npt.NDArray[float] = result.density.y.flatten()
@@ -488,18 +488,8 @@ def zfpkm_plot(results: dict[str, _ZFPKMResult], *, output_png_dirpath: Path, pl
         max_fpkm: float = float(y.max())
         max_fitted: float = float(fitted.max())
         scale_fitted: float = fitted * max_fpkm / max_fitted
-        to_concat.append(pd.DataFrame({"sample_name": name, "log2fpkm": x, "fpkm_density": y, "fitted_density_scaled": scale_fitted}))
-
-        to_concat.append(
-            pd.DataFrame(
-                {
-                    "sample_name": [name] * len(x),
-                    "log2fpkm": x,
-                    "fpkm_density": y,
-                    "fitted_density_scaled": scale_fitted,
-                }
-            )
-        )
+        to_concat[i] = pd.DataFrame({"sample_name": name, "log2fpkm": x, "fpkm_density": y, "fitted_density_scaled": scale_fitted})
+
     mega_df = pd.concat(to_concat, ignore_index=True)
     mega_df.columns = pd.Series(data=["sample_name", "log2fpkm", "fpkm_density", "fitted_density_scaled"])
     mega_df = mega_df.melt(id_vars=["log2fpkm", "sample_name"], var_name="source", value_name="density")

From 833760bcb6c9bf0d0f33f32130f9ea0b92b06dec Mon Sep 17 00:00:00 2001
From: Josh Loecker <joshloecker@icloud.com>
Date: Mon, 6 Oct 2025 15:59:32 -0500
Subject: [PATCH 345/345] fix: do not install on python 3.10

Signed-off-by: Josh Loecker <joshloecker@icloud.com>
---
 .github/workflows/continuous_integration.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/continuous_integration.yml b/.github/workflows/continuous_integration.yml
index 0db91dea..286f54d0 100644
--- a/.github/workflows/continuous_integration.yml
+++ b/.github/workflows/continuous_integration.yml
@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ "3.10", "3.11", "3.12", "3.13" ]
+        python-version: [ "3.11", "3.12", "3.13" ]
     steps:
       - name: Checkout
         uses: actions/checkout@v4